Agricultural Internet of Things-Enabled Smart Greenhouse Systems: A Comprehensive Review of Sensing Technologies, Intelligent Architectures, and Autonomous Control Strategies

Agriculture is gradually transforming under several converging pressures-technological upheavals, climate change, population increases, and depletion of natural resources. Traditionally, these factors have encountered areas of ample land, among other food-farming enterprises and distribution modes. Now, the telling changes have imposed on its disadvantaged workings of feeding a growing world population and environmental sustainability. Hence appeared Agriculture 4.0: cyber-physical systems, real-time data, and precision agriculture, all teaming up in the name of productivity, resource efficiency, and environmental stewardship ‎‎[1]. Therefore, resilient and efficient agricultural systems must be sought to buffer against climatic variability that can maximize yields with as few inputs as possible for global food security.

The cultivation of plants under controlled conditions offers a solution to these challenges, with the greenhouse being a recognized example. The history of greenhouse cultivation unfolds from times of the Romans through the 17th century across Europe with the major developments in the design of glasshouses ‎[2, 3]. Greenhouses first relied on passive solar heating methods and manual control of the environment but entered the industrial age with the introduction of electric heat, artificial lighting, and mechanical ventilation, allowing for operation throughout the year in varying climates. The beginning of the 21st century marked the era of digital technology introduction, thus creating semi-automated greenhouses, able to control temperature, humidity, light intensity, and CO₂ concentration with high accuracy ‎‎[4]. Thus, fully automated smart greenhouse environments were prepared for their establishment.

Now, a smart greenhouse is a cyber-physical system of an unprecedented level of architecture. It comprises many sensors, actuators, microcontrollers, and wireless communication networks to constantly measure and control the critical agronomic variables. Parameters of the environment include temperature, relative humidity, and light, while soil parameters include moisture, pH, and NPK nutrient values-measured through sophisticated electrochemical and capacitive sensors ‎‎[5, 6]. These data are then pushed to a cloud environment or an edge computing device for storage, visualization, and real-time analytics via Internet of Things (IoT)-based frameworks. The availability of low-priced microcontrollers such as ESP32 and Arduino, with communication protocols such as Message Queuing Telemetry Transport (MQTT) and Long-Range Wide Area Network (LoRaWAN), has democratized the smart greenhouse technology so that it would no more be an exclusive technology, only possible for a large-scale farmer ‎‎[7]. It is the AI implementation that truly represents a paradigm shift for greenhouse technology. Thus, enabling intelligent decision-making above and beyond simple automation. Machine learning and deep learning algorithms ingest multi-modal sensor data as inputs to predict environmental conditions, detect abnormalities, and plan irrigation and fertigation schedules ‎‎[8, 9]. Computer vision, conversely, allows for real-time monitoring of plant health, disease diagnosis, and phenotypic analysis using typically large annotated datasets and edge computing for low-latency inference ‎‎[10, 11]. Reinforcement learning methods and fuzzy logic also enhance climate management dynamics in greenhouses ‎[12]. AI-based decision support tools, on the other hand, forecast weather, use historical crop data, and remote sensing data to prescribe precise location-specific management interventions. The integration of AI with IoT and edge computing has led to the development of autonomous greenhouses capable of self-regulation and adaptive response thus heralding the future of controlled-environment agriculture.

Reviews of smart greenhouse technology have tended to concentrate on a single strand, whether IoT architectures, a particular sensing modality, or the application of AI in agriculture, and to present the available techniques rather than weigh them against one another. Recent work such as El Ouaham et al. ‎‎[9] studied AI applications, spectral sensing, and data fusion in considerable breadth. The present review is intended to be complementary. It brings sensing, connectivity, and intelligence into a single organizing scheme rather than treating them as separate field it gathers the surveyed studies into one comparative table containing researches validated results so that recurring weaknesses and the gaps between them become easy to see; and it reads the evidence conditionally, noting not only which methods and sensors perform best but the data, cost, and deployment circumstances under which each does so, before drawing out the questions that remain open. The aim throughout is a practical basis for designing intelligent, resource-efficient greenhouses rather than an inventory of what is available.

1.1 Scope and taxonomy

To move beyond a chronological catalogue of technologies, this review organizes the literature along four orthogonal axes: (i) the AI/analytics technique employed, ranging from rule-based control through classical machine learning and deep learning to reinforcement learning; (ii) the IoT architecture, spanning wired versus wireless, stand-alone versus networked, and edge versus cloud deployment; (iii) the sensing modality, from environmental and soil probes to Red, Green, Blue (RGB), multispectral, and thermal imaging; and (iv) the deployment/autonomy level, from passive climate moderation to fully autonomous, self-optimizing systems. Each subsequent section is framed against these axes, and the researches validated results comparison table positions the surveyed studies within this scheme so that recurring strengths, shared limitations, and open gaps become explicit.

A data acquisition system (DAS) is best understood layer by layer: the sensors that capture field conditions, the microcontrollers that process the readings, and the protocols that carry data to cloud or edge platforms. DASs in smart greenhouses usually measure some parameters such as temperature; humidity; light intensity; CO₂ concentration; soil moisture content; nutrient analysis (NPK); and other minor indicators for plant health, such as chlorophyll fluorescence - low at the current level of greenhouse research activity [‎21, ‎22].

The main objectives of a DAS in a greenhouse environment are centered on ensuring efficient, informed, and traceable operation of the cultivation process. First, DAS enables continuous monitoring through the real-time collection of environmental and soil parameters, providing constant situational awareness of greenhouse conditions. These measurements serve as the foundation for effective control, as the acquired data are used as inputs for automated control systems that regulate actuators such as irrigation, ventilation, heating, and lighting to maintain optimal growing conditions. Beyond real-time operation, DAS supports in-depth analysis by enabling data-driven trend evaluation and the training of AI and predictive models for crop growth, stress detection, and yield optimization. Finally, the system plays a critical documentation role by storing historical data for quality assurance, scientific research, compliance with standards, and certification purposes, ensuring transparency and traceability in greenhouse management.

The DAS will be performed not only for actuating the devices into action such as fan, irrigation, lighting, and so on, but also to integrate with forecasting models and ’alarms’ of disease out- breaks, irrigation schedules, and optimization of fertilization in use ‎[23].

3.1 Architectures

The architecture of a DAS varies mainly with respect to complexity, cost, scale, and required response time. Generally, as illustrated in Figure 1, the system comprises four core layers.

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Figure 1. Layers of Internet of Things (IoT) architecture [24]

The sensor layer forms the foundation of the system and consists of physical sensing devices such as temperature and humidity sensors (e.g., DHT22), electrical conductivity and pH probes, and light sensors that directly capture environmental and soil conditions. Data from these sensors is then gathered by the data collection layer, which relies on microcontrollers or data loggers such as Arduino, ESP32, or Raspberry Pi to acquire, digitize, and organize the measurements. Once collected, the processing layer handles local or edge-level computation, where raw data may be filtered, aggregated, and analyzed to support basic decision-making before transmission. Finally, the communication layer enables the exchange of information between devices and external platforms using protocols and technologies such as MQTT, Hypertext Transfer Protocol (HTTP), LoRa, Wi-Fi, or ZigBee, ensuring reliable data flow to cloud services or user applications.

3.1.1 Manual vs. automated

In early greenhouse monitoring operations, manual data- logger methods were applied; growers would often visually inspect analog instruments or use hand-held sensors. Manual data logging is laborious, prone to error, and lacks good time resolution ‎[25].

On the contrary, the automated DAS constantly acquire and record data with minimal human involvement using microcontroller-based systems. Table 1 contrasts manual and automated acquisition: automated systems deliver higher accuracy, high-frequency sampling, and immediate algorithmic control at far lower labor. For example, automated irrigation based on real-time soil moisture readings substantially improves water-use efficiency ‎[26].

Studies report yield improvements of 15–30% when transitioning from manual to automated greenhouse monitoring due to improved climate stability and timely interventions.

Table 1. Manual vs. automated data acquisition systems (DASs) comparison

3.1.2 Wired versus wireless

In wired systems, data transmission can be carried out via RS-232, RS-485, or Ethernet cables. They are immune to electromagnetic interference and are suitable for static installations. Nevertheless, they suffer from limited scalability with installation difficulties and high maintenance in unfavorable conditions.

Wireless DAS employ protocols that include ZigBee, Wi-Fi, LoRaWAN, or Bluetooth Low Energy (BLE). These wireless systems provide modularity and scalability, particularly for large scale undertakings and modular greenhouses. Wireless DAS reduce installation cost and promote remote monitoring through cloud platforms ‎[27]. LoRa provides long range, low power communication, which is well suited to agricultural ap- plications ‎[28]. Table 2 compares the two-options wireless adding modularity, scalability, and remote monitoring, wired offering immunity to interference for static installations.

Table 2. Wired vs. wireless data acquisition systems (DASs) comparison

Validated findings:

•Wired RS485 systems show <1% data loss in humid greenhouse environments ‎[29].

•Wireless LoRa-based DAS reduce installation cost by up to 40%, but introduce latency unsuitable for fast control loops ‎[18].

3.1.3 Stand-alone vs. networked

A stand-alone DAS carries out all data logging and analysis locally without communicating with any external entity. Such systems are helpful in remote locations or research setups where internet connectivity is spotty. Through SD cards or local databases, data is stored and periodically retrieved.

Networked DAS are viewed as part of a wider cyber-physical system that generally connects to the Internet or local servers. Table 3 sets stand-alone against networked DAS, the latter adding remote access, cloud storage, real-time dashboards, and machine-learning support. These cloud-connected DAS provide predictive analytics and decision support tools vital to modern precision agriculture.

Table 3. Standalone vs. networked data acquisition systems (DASs) comparison

The enhancement of edge computing in networked DAS allows preliminary processing at the sensor node, therefore, diminishing latency and bandwidth usage, enabling faster action response ‎[30].

Validated findings:

•Networked systems enable:

•Multi-zone climate optimization

•Cross-greenhouse analytics

Predictive control using AI models

Field studies report 20-35% water and fertilizer savings in networked DAS compared to stand-alone controllers ‎[19]. Figure 2 lays out the general IoT architecture underlying these greenhouse deployments.

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Figure 2. Internet of Things (IoT) general architecture for greenhouse applications

3.2 Core components

A DAS operates with efficiencies and powers relying heavily on the choice of an arrangement of these major portions. It has four core layers containing building blocks which are sensors, computational units, and communication protocols. Each performing a very concrete obligation in the realization of environmental and agronomic data acquisition from the field to platforms for analysis [31].

3.2.1 Sensors

Sensors form the foundation of any DAS, as they provide the essential measurements required for monitoring and controlling agricultural environments. They can be broadly categorized into environmental sensors, soil sensors, and plant-specific sensors.

Environmental sensors. Environmental sensors capture the surrounding atmospheric conditions that directly influence plant growth and the prevalence of pests and diseases. For instance, temperature and humidity sensors such as the DHT22 and SHT31 are widely deployed due to their low cost and reliability. Light sensors, such as the BH1750 and TSL2561, are commonly used to measure light intensity, while BMP280 and BME680 sensors provide additional measurements such as barometric pressure, air quality, and volatile organic compounds. Monitoring these parameters allows precise climate control in greenhouses, including heating, ventilation, and pest management strategies.

•Temperature and humidity: DHT22, SHT31, SHT35.

•Light intensity: BH1750, TSL2561.

•Air quality/pressure: BME680, BMP280.

Soil sensors. Soil sensors provide critical insights into the underground growing medium, ensuring optimized irrigation and fertilization management. The most widely used devices include the Capacitive Soil Moisture Sensor v1.2, which measures volumetric water content without corrosion issues, and electrical conductivity (EC) sensors such as the Decagon 5TE and WET150, which measure EC, moisture, and temperature simultaneously. pH probes (e.g., Atlas Scientific pH Sensor) are widely used for soil acidity monitoring, while advanced nutrient sensors such as Ion-Selective Electrodes (ISE) are employed for detecting nitrogen, phosphorus, and potassium (NPK) concentrations.

•Moisture: Capacitive Soil Moisture Sensor v1.2, Decagon EC-5.

•Electrical Conductivity (EC): Decagon 5TE, WET150. pH: Atlas Scientific pH Sensor.

•Nutrient Sensors (NPK): Ion-Selective Electrodes (ISE).

Plant-specific sensors. Plant-specific sensors directly assess crop physiological status and health. Optical sensors are among the most widely applied in this category. For example, chlorophyll meters such as the SPAD-502 Plus are used to estimate leaf chlorophyll con- tent, providing insights into plant nitrogen status. Multispectral and hyperspectral imaging sensors, including Green Seeker and UAV-mounted Normalized Difference Vegetation Index (NDVI) cameras, enable large-scale monitoring of crop vigor through vegetation indices such as the NDVI. Fluorescence-based systems, such as PAM fluorometers, are also employed to evaluate photosynthetic efficiency.

•Chlorophyll Content: SPAD-502 Plus.

•Vegetation Indices: Green Seeker, UAV NDVI cameras.

•Fluorescence: PAM fluorometers.

Long-term usability for these sensors calls for considerations pertaining to environmental tolerance, drift behavior, and maintenance requirements when in a humid, dusty, or saline setting.

3.2.2 Microcontrollers

Microcontrollers are the processing core of DAS setups. They interpret raw sensor readings, apply local logic (such as thresholds or edge computing algorithms), and oversee data transmission. Typical options found in agricultural settings include:

•Arduino boards (like the Uno and Nano) are perfect for simple sensing applications where simplicity and community support are essential.

•ESP32/ESP8266 modules integrate Wi-Fi or Bluetooth and advanced power saving modes for battery-operated IoT devices.

•Raspberry Pi systems have higher computational power and are used in image processing databases or local AI model operation ‎[32].

The ultimate choice depends on the project at hand, either simple data logging or complex on-site analytics.

3.2.3 Communication protocols

Immediately after processing, the next task is to transmit the sensor data to the external systems. The means of communication depends on the distance, energy constraints, and data frequency. Some of the important protocols are:

MQTT Protocol. The MQTT is an open-source and lightweight communication protocol specifically designed for low-bandwidth, high-latency, and resource- constrained applications. It has become one of the most widely adopted protocols in IoT ecosystems, including smart greenhouse and precision agriculture deployments.

Unlike traditional client-server models, MQTT follows a publish-subscribe communication paradigm, where devices (clients) publish messages to specific topics, and other devices subscribe to those topics to receive the data. This is mediated by a central component known as the broker. Popular brokers such as Eclipse Mosquitto and HiveMQ are often deployed in agricultural IoT systems due to their scalability and reliability. The broker decouples publishers from subscribers, thereby reducing communication overhead and improving system flexibility.

Figure 3 shows how MQTT's publish–subscribe model links publishers and subscribers through a central broker. The publisher is a client that is supposed to send data to the network, such as the ESP32 sending soil moisture readings. The messages are received by a central broker that acts as an intermediate server, which takes in the incoming data and forwards them to all interested parties. A typical example is the Mosquito broker, which is either run locally or in the cloud. The subscriber is a client on the receiving end listening to particular topics to obtain the published data, which could be, say, a mobile application showing the real-time humidity values.

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Figure 3. Message queuing telemetry transport (MQTT) clients ‎[38]

Corresponding to the various needs of applications, MQTT defines three different quality levels of service (QoS), which determine signaling and delivery reliability of messages. QoS 0 is at best-effort with no acknowledgments so that message delivery is useful for noncritical data, allowing for an occasional loss. QoS 1 guarantees message at least once delivery but presumably will cause duplicate messages, while QoS 2 is the reliable way of defining on time delivery with the cost of higher communication overhead. The whole range of QoS options enables system designers to make compromises between redundancy and bandwidth efficiency, which is especially important in agricultural environments with often unstable or intermittent network accessibility.

One of the main advantages of MQTT is that it is extremely lightweight, making it particularly suitable for resource- constrained devices such as Arduino and ESP32 ‎[33]. Its efficiency stems from the fact that its message header is only 2 bytes, ensuring minimal overhead and reduced bandwidth consumption ‎[34]. Moreover, MQTT supports bi-directional communication, which enables not only sensor data monitoring but also remote actuation of devices, a key requirement in modern IoT and smart agriculture systems ‎[35]. Another strength is its scalability: MQTT can operate effectively from just a few devices to thousands of interconnected nodes without significant degradation in performance ‎[36].

Despite these benefits, MQTT also has some limitations. A major drawback is its reliance on a broker to manage communication, which introduces a single point of failure in the system. Furthermore, MQTT lacks built-in encryption and security mechanisms; security is usually enforced through the integration of TLS/SSL, which can be resource-intensive for constrained devices ‎[37]. Finally, while MQTT is highly efficient for lightweight messaging, it is not optimized for very high- throughput or real-time streaming applications when compared to alternatives such as WebSocket or AMQP ‎[37].

LoRaWAN Protocol. LoRaWAN is a communication protocol specifically designed for low-power, long- range wireless IoT applications ‎[39]. It is built on top of the Long Range modulation technique, which uses Chirp Spread Spectrum (CSS) modulation to achieve reliable data transmission over distances of up to 15 km in rural areas while consuming minimal energy ‎[40]. These features make LoRaWAN particularly well suited for large-scale smart agriculture and agribusiness applications where traditional connectivity options (e.g., Wi-Fi or cellular) are either unavailable or cost prohibitive.

LoRaWAN operates in unlicensed Industrial, Scientific, and Medical (ISM) frequency bands, typically 868 MHz in Europe, 915 MHz in North America, and 433 MHz in parts of Asia, making it a cost-effective solution for IoT deployments. Its star-of-stars topology allows end devices to communicate with gateways, which then forward data to a central network server, ensuring simplified and scalable system design.

Figure 4 sets out the LoRaWAN architecture and its four components end devices, gateways, the network server, and the application server. The End Devices are typically battery-powered sensor nodes such as soil moisture sensors or weather stations that send data at intervals. The devices communicate with the Gateways, which relay the received LoRa-modulated signals to the Network Server over IP-based backhaul connections, such as Ethernet, Wi-Fi, or cellular networks. The Network Server is at the heart of the system with responsibility for managing the connected devices, filtering duplicate packets, and enforcing security mechanisms. The Application Server receives the processed sensor data from the Network Server and presents it to the end user applications for visualization, decision-making, and actuation.

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Figure 4. Architecture of a network implementing the Long Range Wide Area Network (LoRaWAN) protocol‎ [41]

LoRaWAN defines three classes of devices in order to cater to different power and communication requirements. Class A devices are the most power-limited; they briefly open their receive windows only after having transmitted their data. This makes class A devices suitable for sensors having a long battery life requirement. Class B devices have scheduled receive windows added for more downlink communication. Class C devices, however, keep their receivers open most of the time, thereby consuming more power and incurring less delay-more beneficial for actuation-oriented applications. The main advantages of LoRaWAN are its long communication range and ultra-low power consumption, allowing end devices to operate on batteries for up to 10 years. It also supports large-scale deployments, with a single gateway capable of handling thousands of nodes, making it highly suitable for monitoring extensive agricultural fields. Adaptive data rate mechanisms further optimize communication efficiency and battery life by adjusting transmission parameters dynamically [40]. Moreover, LoRaWAN incorporates security at both the network and application layers using AES-128 encryption, ensuring data integrity and confidentiality.

However, LoRaWAN also has notable limitations. Its data rates are relatively low (ranging from 0.3 kbps to 50 kbps), restricting it to small, infrequent sensor transmissions rather than bandwidth-intensive applications. It can also suffer from increased latency, particularly in Class A devices, which is unsuitable for time-critical control operations. Additionally, since LoRaWAN operates in unlicensed spectrum, it is subject to duty-cycle restrictions and interference, which may cause network congestion in densely deployed areas. These tradeoffs make LoRaWAN ideal for large-scale, low-data-rate sensing in agriculture, but less appropriate for applications requiring real time communication.

HTTP Protocol. The HTTP is one of the most established and universally adopted communication protocols in Internet-based systems and continues to play an important role in IoT deployments, including smart greenhouse applications. Despite being comparatively more resource-intensive than lightweight protocols such as MQTT or CoAP, HTTP remains highly relevant due to its seamless integration with cloud infrastructures, RESTful APIs, and visualization dashboards [42, 43]. Its ubiquity makes it particularly advantageous in Wi-Fi-enabled greenhouse environments, where sensor nodes and microcontrollers (e.g., ESP32 or Raspberry Pi) can transmit data directly to web servers or cloud platforms without re- quiring additional middleware.

HTTP is based on a client-server communication model, in which IoT devices act as clients that initiate requests (e.g., GET, POST, PUT) to servers that process and store the data. This paradigm enables real-time greenhouse sensor readings, such as temperature, humidity, and soil moisture, to be uploaded to cloud dashboards for monitoring, visualization, and analytics. The standardized request methods provide an efficient structure for interaction with web services, making HTTP especially suitable for integration with existing data management and decision-support platforms.

In the context of smart agriculture and green- house IoT systems, an HTTP-based architecture typically includes three primary elements: the client, which corresponds to IoT devices responsible for sensing and transmitting environ- mental data; the server, often deployed on cloud infrastructures such as Amazon Web Services (AWS) IoT Core or Microsoft Azure IoT Hub, where data is aggregated and processed; and the user interface, which can take the form of mobile or web-based dashboards that support real time decision making [42]. This architecture leverages the maturity of HTTP and its compatibility with web technologies to ensure system scalability and accessibility.

Figure 5 traces HTTP's synchronous request–response exchange: unlike MQTT's publish-subscribe model, the server replies only when the client queries it. While this approach facilitates straightforward integration with web services, it also results in higher latency and energy consumption in scenarios requiring frequent or continuous data exchange ‎[44]. In greenhouse environments where sensors generate frequent updates, this overhead can re- duce efficiency compared to event-driven protocols.

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Figure 5. Hypertext transfer protocol (HTTP) request/response protocol

The main advantage of HTTP lies in its universality: it is widely supported across all devices, operating systems, and cloud platforms, thereby reducing complexity in the design and deployment of greenhouse IoT systems. HTTP also offers robust interoperability with web applications and APIs, simplifying integration with third-party services and decision support tools. Furthermore, the maturity of HTTP ensures stability, reliability, and security mechanisms through HTTPS, which protects sensitive agricultural data transmitted over public net- works [‎45].

On the other hand, HTTP presents several challenges for IoT applications in agriculture. Its relatively high bandwidth consumption, due to large header sizes, is inefficient in low power or low-bandwidth environments. The synchronous communication model also makes HTTP less suitable for real-time control applications, where protocols such as MQTT or CoAP provide better responsiveness and energy efficiency. Additionally, while HTTPS enables secure communication, the computational overhead of TLS/SSL encryption can be taxing for microcontrollers with constrained resources, thus limiting scalability in resource-limited deployments ‎[46].

For these reasons, HTTP is most often adopted in hybrid greenhouse communication architectures, where it complements lightweight protocols: frequent sensor updates are typically handled via MQTT or LoRaWAN, while HTTP is employed for higher-level operations such as device configuration, external API integration, or data visualization on web-based dashboards.

No single protocol is optimal: as Table 4 shows, MQTT, LoRaWAN, Wi-Fi, and BLE each trade off range, energy, and throughput differently. MQTT minimizes overhead for frequent, small telemetry but presumes reliable connectivity and a broker. LoRaWAN reaches kilometers at milliwatt power, but its low data rate and duty-cycle limits rule out image or high-frequency streams. Wi-Fi and BLE offer higher bandwidth over short ranges at greater energy cost. The choice therefore depends on the deployment scenario: dense, powered, single-structure greenhouses favor MQTT over Wi-Fi, sparse off-grid or multi-hectare sites favor LoRaWAN, and image-heavy phenotyping pipelines require higher-bandwidth links or local edge processing to avoid the network entirely.

Table 4. Communication protocols comparaison

3.2.4 Data management

With smart agricultural systems producing immense quantities of data, data management not only takes on a critical role in real-time control but also long-term analysis, optimization, and traceability. Raw data must be analyzed, but it must also be efficiently processed, stored, and retrieved to squeeze out value. This section will explore, in depth, the three pillars of modern data management for greenhouse systems: edge computing, cloud storage, and database design.

Edge processing. Edge processing entails analyzing and interpreting sensor data at or near the source, almost always on microcontrollers, embedded systems (Raspberry Pi, etc.), or edge gateways. By this method, latency, bandwidth consumption, and reliance on a constant internet connection are much lessened ‎[47].

In a greenhouse, an edge-enabled microcontroller might evaluate temperature and humidity readings every few seconds locally and turn on a cooling fan or irrigation system when exceeded thresholds have been set. This will enable real-time control fast into remote areas that may not have access to the cloud. Furthermore, preprocessing at the edge-such as noise filtering, thresholding, or extracting trends-decreases the volume of data sent over to the cloud, making for a more efficient and hardened system.

Advanced edge systems can support light machine-learning models such as TensorFlow Lite or Tiny machine learning, for detecting anomalies or predicting events, such as a pest outbreak, which provides more autonomy at the device level ‎[47].

Cloud storage. Cloud computing complements edge processing by providing massive on-demand storage and access to standardized environments with the computational power required to run complex analytics. Platforms such as AWS, Microsoft Azure, and Google cloud.

In the greenhouse context, cloud storage serves multiple purposes:

•Historical Data Logging: Enables tracking of growing conditions and system performance over extended periods, including weeks, seasons, or even years.

•Multi-user Access: Multiple stakeholders such as farmers, agronomists, and researchers can remotely monitor and collaborate using the same data.

•Backup and Redundancy: Ensures data reliability by duplicating records on geographically distributed servers in the event of local device failure.

Additionally, cloud platforms can integrate with third-party applications such as weather forecasting APIs, agricultural databases, and market prediction models, further enhancing data-driven decision-making.

3.3 Database design

Database design. The effectiveness of the database design is, here, the pillar to allow timely storage, retrieval, and analysis of data. Most data flowing into the greenhouse environment is of time-series nature, acquired through sensors at regular intervals. Influx DB, Time scale DB, and OpenTSDB are some examples of time- series databases (TSDBs) that are best suited for this type of structured temporal data [48]. They offer high throughput in writing and instantaneous querying of trends, averages, and thresholds over time.

A well-designed agricultural database would encompass:

•Sensor Metadata: Location, type, and calibration information.

•Data Schema: Usually follows a template like (times- tamp, sensor ID, parameter, value, unit).

•Indexing: For a fast retrieval based on sensor location, type, or time range.

Hybrid relations seem to have been adopted by some systems; for example, MySQL or PostgreSQL might hold user data, configurations, and alerts, while NoSQL or TSDB might be in charge of the continuous flow of sensor inputs. Overall, normalization helps remove data redundancy, increases storage optimization, and hence enhances the scalability of the system. Finally, the synergy of edge computing, cloud services, and an appropriately designed database ensures that smart green- house systems can react, prophesy, and be prepared for expansion.

This section moves from classical machine learning toward deep and reinforcement learning, emphasizing where each is justified by data availability and task type. Due to the increasing demand for the sustainable course of agriculture, an impetus is created for introducing a precision farming ideology that uses machine learning models for crop management from sensor data analysis and optimization of resource use. In smart greenhouses, real-time actionable insights obtained from interconnected sensors and predictive algorithms can identify nutrient deficiency, predict environmental change, and activate the control system, thereby enhancing yield and reducing waste ‎[53]. This chapter would lay the foundation for coupling state-of-the-art deep learning with robust sensor networks to predict NPK deficiencies in lettuce and maintain ideal growing conditions using an intelligent IoT architecture ‎[56].

5.1 Fundamentals of machine learning

As illustrated in Figure 7, modern machine learning systems are structured as pipelines with interdependent stages: problem scoping, data collection and engineering, modeling, evaluation, and deployment [57]. From a software engineering perspective, this workflow covers the entire lifecycle of model development and constitutes one re- producible and automated methodology for the development of concrete and working machine learning applications. The pipeline structure ensures that all components are executed accordingly, thereby putting arsenal into the hands of developers and data scientists in the end-to-end development and experimentation life cycle ‎[58]. For example, in smart agriculture, using pipelines allows machine learning algorithms such as yield prediction, regression models to be incorporated using historical data like soil quality and weather conditions to forecast parameters such as crop productivity ‎[59]. Thus, machine learning pipelines help in the technicalities but also facilitate the actual decision- making and resource optimization in different fields.

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Figure 7. Machine learning workflow diagram‎ [60]

5.1.1 Data collection

Data collection entails acquiring raw observations (e.g., sensor readings, images, logs) and, increasingly, pairing labels via crowdsourcing or weak supervision techniques. Related recent surveys highlight developments in labeling techniques, with automatic and human-in-the-loop processes alike, to counteract bottlenecks in large-scale data acquisition, emphasizing strategies for cost-efficient acquisition of top-notch data annotation ‎[55].

Data acquisition. Data acquisition encompasses three main strategies:

•Data discovery: Finding and accessing datasets of interest from different sources structured like relational databases, unstructured like social media streams, or coming in from a sensor network often through collaborative sites like DataHub, which supplies search, versioning, and integration tools to ease dataset discovery while guaran- teeing dataset provenance.

•Data augmentation: To enlarge artificially existing datasets through, e.g., geometric/image transformations,GAN-based sample synthesis, or rule-based perturbations, increasing diversity tremendously without any further efforts for data collection.

•Data generation: Complete data synthesis with the help of simulators or procedural models (e.g., rendering engines for computer vision), which allows the controlled exploration of corner-case scenarios very rarely or expensive to capture in the real world.

Crowdsourcing. Crowdsourcing distributes the labeling tasks to an online labor pool in large numbers, then uses redundancy, aggregation, and active learning to keep the quality while scaling. Key platforms include:

•Revolt: Executes repeated labeling and smart label aggregation; eliminates or down-weights bad contributions to increase final label quality.

•CrowdER: Its specialty is entity resolution-using human judgment to merge records that identify the same real- world entity and hence, disambiguate records from the automated perspective.

•Qurk: Brings crowdsourcing into database operations. It provides interfaces where users can hand out tasks and automatically ensures there is enough redundancy and consensus on what the correct labels are.

Weak supervision. Weak supervision methods produce large volumes of labels through programmatic rules or noisy sources, and then statistically model to refine the labels:

•Data programming (e.g., Snorkel): The user writes a set of labeling functions which are heuristics, regex patterns, or weak classifiers that label data with noisy labels. Then a generative model estimates the accuracy and correlation of these functions to produce probabilistic labels, which are subsequently used in a downstream discriminative model for robust training from large datasets.

•Fact extraction and distant supervision: Systems like Never-Ending Language Learning (NELL) and Knowledge Vault crawl unstructured web text, extracting entity- relation facts. These facts become seed labels with which to annotate large corpora automatically (distant supervision), trading noise for scale while providing useful initial annotations.

Hence, the more these kinds of acquisition and labeling strategies become combined, the more practitioners could create large-scale datasets enriched with annotations, even if they lack resources for this undertaking. This, in turn, gives machine learning a robust foothold applying generalization.

5.1.2 Data preprocessing

Preprocessing is a set of crucial tasks conducted on raw data to convert it into the input format while considering the missing values, conversion of features, and elimination of outlying observations. These operations reduce noise and bias, lowering errors and increasing generalization.

Yandrapalli [61] proposed an automated framework for data quality assessment through a bouquet of statistical and machine learning techniques. This framework combined irrespective outlier detection methods: Tukey’s IQR, which had the highest sensitivity towards extreme values, Isolation Forest (IF), being a tree-based anomaly-detector, and DBSCAN, being a density-based clustering algorithm. Post-data cleansing, they were then used for training models such as logistic regression, KNN, and Naive Bayes, which displayed an improved performance.

The study evokes the relevance of structured autoscaling be- fore learning and hence established a privileged position for automated governance frameworks in machine learning pipelines ‎[62].

5.1.3 Modeling

In modeling, one selects algorithms such as deep networks, ensemble methods, or support vector machines and trains them to memorize some patterns for prediction. Within the purview of smart agriculture, the most recent research highlights employing artificial intelligence, big data analytics, and remote sensing toward predictive modeling in crop health monitoring, irrigation optimization, and pest management. These modeling techniques become instrumental in enabling the data-driven decision- making and improving efficiency and sustainability in agricultural practices ‎[63].

5.1.4 Evaluation

In smart agriculture and smart greenhouse systems, it is crucial to check on machine learning performances to be able to as- certain their reliability and adaptability towards real world variability. Models ought to perform beyond just the simple classification with respect to other environmental conditions and sensor discrepancies

Classification metrics

•Accuracy: Measures the proportion of correctly predicted instances out of the total number of predictions. While intuitive, it may be misleading when class distributions are imbalanced (for example nutrient deficiency is rare).

Accuracy $=\frac{T P+T N}{T P+T N+F P+F N}$        (2)

Precision and Recall

•Precision: Evaluates the proportion of true positives among all predicted positives. Crucial when false positives are costly

Precision $=\frac{T P}{T P+E P}$        (3)

•Recall (Sensitivity): Measures the proportion of true positives identified among all actual positives. Important when missing a positive case (e.g., nutrient deficiency) has serious implications

Recall $=\frac{T P}{T P+F N}$        (4)

•F1-Score: The harmonic mean of precision and recall. Balances both false positives and false negatives, making it especially useful for imbalanced classification tasks such as nutrient deficiency detection.

$F 1=\frac{2 * \text { Precision} * \text { Recall}}{\text { Precision}+ \text { Recall}}$       (5)

•Root Mean Squared Error (RMSE): A widely used metric in regression problems. It penalizes larger errors more than smaller ones, making it suitable for yield prediction or continuous soil nutrient monitoring.

$R M S E=\sqrt{\frac{1}{n} \sum_{i=1}^n\left(y_i-\hat{y}_i\right)^2}$       (6)

•Mean Absolute Error (MAE): Average absolute difference between predicted and actual values. Robust to outliers

$M A E=\frac{1}{n} \sum_{i=1}^n\left|y_i-\hat{y}_i\right|$        (7)

•R² (Coefficient of Determination): Indicates the pro- portion of variance in the dependent variable that is predictable from the independent variables. Useful in evaluating environmental forecasting models.

$R^2=1-\frac{\sum_{i=1}^n\left(y_i-\hat{y}_i\right)^2}{\sum_{i=1}^n\left(y_i-\bar{y}_{\imath}\right)^2}$        (8)

•Receiver Operating Characteristic–Area Under Curve (ROC-AUC): Measures the ability of a classifier to distinguish between classes, independent of decision threshold.

$\int_0^1 T P R(F P R) d(F P R)$         (9)

$T P R=\frac{T P}{T P+F N}, \quad F P R=\frac{F P}{F P+T N}$         (10)

8.png

Figure 8. Confusion matrix ‎[60]

The confusion matrix is a fundamental tool for visualizing the performance of classification models, especially in multi-class or imbalanced scenarios common in smart agriculture. As illustrated in Figure 8 the counts of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) predictions, allowing a detailed error analysis. By examining the confusion matrix, practitioners can identify which classes the model confuses most often and tailor improvements accordingly. For example, in nutrient deficiency detection, understanding whether the model tends to miss deficient cases (high FN) or wrongly flags healthy plants (high FP) is critical for practical deployment.

5.1.5 Statistical comparison tests

Table 5 lists the statistical tests used to compare models when differences are subtle.

Table 5. Statistical tests for model comparison

To assess systematically on performance, one needs to set up notation or metrics for both classification and regression tasks. According to the previous study ‎[63], in a 2024 Frontiers in Bioinformatics re- view, one must be wary of overly relying on single metrics, such as accuracy, especially when imbalanced sets or non-stationary conditions plague the data ‎[64]. It is instead advised that stratified cross-validation and domain-dependent metrics, tailored in the agricultural context, be used, such as those sensitive to the false negative detection of nitrogen deficiency. Similarly, Rainio et al. [62] evaluates such evaluation strategies across crop diagnostics and underscores the need for real-field testing beyond the lab to assess real robustness and transferability.

Evaluation protocols typically integrate k-fold cross- validation, domain-specific robustness tests, and sensitivity analyses to simulate deployment variability, and thus they as- sure that the models are not only statistically valid but agronomically viable.

5.1.6 Deployment

Deployment involves putting the validated model into a live production environment for real-time inference. There are of- ten prerequisites like containerizing, orchestrating APIs, and setting up continuous monitoring infrastructure to ensure scalability and reliability. According to ‎the study [65], there are three major phases in deployment: integration, monitoring, and updating. Integration often throws up challenges in configuration debt, tangled pipelines, and lack of collaboration between data science and engineering teams. Monitoring comes with machine learning-specific metrics like data drift, bias, and prediction performance, which are much harder to either define or act upon as compared to traditional software logs. Updating models in response to concept drift remains a weak point in many machine learning systems, with few organizations supporting seamless retraining and rollback.

In contrast, the study ‎[66] proposed a structured framework to assess deploy ability through risk analysis wherein operational scenarios are defined with potential damage estimated, and Key Risk Indicators (KRIs) computed by weighting these risks by likelihood. The framework explicitly measures robustness to input corruptions, distributional shifts, and adversarial perturbations, providing a standardized way to compare deploy ability across systems.

The combined insights from these two studies reveal both the practical and theoretical underpinnings of machine learning deployment. On the practical side, containerization (Docker, Kubernetes), as well as API orchestration, are essential for modularizing model infrastructure and supporting real-time inference. However, as studied by Paleyes et al. [64], those are not a silver bullet and must be integrated with careful attention to system architecture and team workflow. On the theoretical side, the deploy ability framework by Heymann et al. [66] enabled organizations to quantify the risk associated with deployment decisions and infrastructure choices, thereby formalizing the assessment of operational robustness.

5.2 Learning paradigms

Learning paradigms stand apart from each other in terms of the availability of training feedback. Historically, three methods were considered the core of complete machine learning: supervised learning, unsupervised learning, and reinforcement learning ‎[67]. Recently, however, semi-supervised methods have gained prominence in addressing the gap between labeled and unlabeled data. Below, each of these paradigms is explored along with examples, methods, and challenges they pose.

5.2.1 Supervised learning

In supervised learning, a model f is trained to associate given inputs-interpreted as x-with the correct outputs or answers, seen as y, provided by a labeled dataset {(x_i, y_i)}. Hence, supervised learning opens the doors to implementing classical algorithms, such as the support vector machine (SVM), decision trees, and random forests, as well as newer deep architectures like convolutional neural networks (CNNs) for images ‎[68].

Supervised learning in smart greenhouses. In controlled agriculture, supervised learning is usually associated with the tackling of crop disease recognition, yield prediction, and environmental control optimization.

Classification. Identifying plant health status (healthy vs. diseased), detecting pest presence, or categorizing crop maturity levels from sensor or image data.

Regression. Predicting continuous variables such as soil moisture, nutrient concentration, or expected yield based on sensor readings and historical records.

Supervised learning algorithms give highly accurate and explainable predictions when an ad- equate amount of labeled data is present. Thus, such methods work well for decision-making in irrigation scheduling, fertilization, and disease detection. However, the major challenge lies in the need for huge amounts of annotated agricultural data. Such data collection is often expensive and lengthy; for in- stance, crop disease diagnosis often requires the input of experienced pathologists. Furthermore, such models can suffer from overfitting in cases of imbalanced datasets (e.g., more healthy samples than diseased), thus limiting their generalization to actual farming conditions.

5.2.2 Unsupervised learning

Unsupervised methods try to find structure in unlabeled data {x_i} by discovering patterns like clusters or low-dimensional manifolds ‎[68].

Key Methods:

•Clustering (e.g., k-means, hierarchical): Groups similar samples without labels.

•Dimensionality Reduction (e.g., PCA, t-SNE): Finds compact representations that preserve variance or neighborhood relationships.

Advanced Example: introduce SwAV, which simultaneously clusters multiple augmented views of each image and enforces consistency between their assignments, eliminating the need for costly pairwise comparisons and large memory banks while achieving 75.3% top-1 accuracy on ImageNet with ResNet-50 [69].

Unsupervised learning is crucial for pretraining representations that can be fine-tuned with limited labels, especially in domains where annotations are scarce.

5.2.3 Semi-supervised learning

Semi-supervised learning (SSL) combines a small labeled set with a larger pool of unlabeled data to boost performance when labels are expensive. Emmert-Streib & Dehmer classify SSL as one of the” modern” paradigms that extend the classical three. Oliveira and Berton provide a detailed taxonomy of SSL methods addressing class imbalance, organizing techniques into eight categories: balancing, graph based, loss based, self-training, ensemble, active learning, postprocessing, and other methods ‎[70].

Common SSL strategies. Several approaches were proposed with an intention to make the best use of the unlabeled data within the usual working arrangements of SSL. An attempt is made to summarize the most commonly used methods under the categories below:

•Self-training: A model is first trained on a rather small labeled dataset and is subsequently applied to assign pseudo- labels to unlabeled samples. An iteratively built-up training set consists of samples carrying confident predictions. Gradually this training will improve the model performance. While it is simple, it also can propagate errors if wrong labels are assigned by the model in the initial steps.

•Graph-based methods: These treat both labeled and un- labeled data points as nodes in a graph, where edges reflect the degree of similarity between instances. Labels then propagate through the graph structure consistent with the underlying assumption that samples that are similar share the same label. Such approach is particularly effective when the relational structure naturally exists in the domain, for example, in sensor networks or biological datasets.

•Loss-based regularization: Instead of just assigning labels, these modern methods tweak the loss function such that smoothness or consistency is applied over un- labeled data. For instance, entropy minimization, which pushes the model toward confident predictions, and consistency regularization, which enforces stability of pre- dictions under data perturbations. Such approaches are widely adopted in modern SSL frameworks due to their flexibility.

•Hybrid strategies: Recent work often combines the above paradigms, e.g., using self-training together with consistency losses, or integrating graph information into pseudo-labeling. These hybrid approaches aim to reduce the weaknesses of individual methods and achieve more robust performance in real-world scenarios

5.2.4 Reinforcement learning

Reinforcement learning has emerged as a powerful paradigm for sequential decision-making problems, where an agent learns by interacting with its environment. At each discrete time step $t$, the agent observes a state $s_t \in S$, selects an action $a_t \in A$, receives a scalar reward $r_t \in R$, and transitions to a new state $s_{t+1}$. The objective is to learn a policy $\pi(a \mid s)$ that maximizes the expected cumulative discounted reward.

•Markov decision process (MDP): reinforcement learning problems are often formalized as an MDP defined by the tuple $(S, A, P, R, \gamma)$, where $S$ is the state space, $A$ is the action space, $P\left(\left.s\right|^{\prime} s, a\right)$ is the transition probability, $R(s, a)$ is the reward function, and $\gamma \in[0,1]$ is the discount factor controlling the importance of future rewards.

•Objective function: The agent aims to maximize the expected return, defined as:

$J(\pi)=\mathbb{E}_\pi\left[\sum_{t=0}^{\infty} \gamma^t r_t\right]$        (11)

•Value functions: The state-value function V^π(s) and the action-value function Q^π(s, a) quantify the expected return starting from state s or state-action pair (s, a), respectively:

$V^\pi(s)=\mathbb{E}_\pi\left[\sum_{t=0}^{\infty} \gamma^t r_t \mid S_0=s\right]$        (12)

$Q^\pi(s, a)=\mathbb{E}_\pi\left[\sum_{t=0}^{\infty} \gamma^t r_t \mid S_0=s, a_0=a\right]$        (13)

•Policy optimization: Policy-based methods adjust parameters θ of a differentiable policy π_θ(a|s) to maximize J(π_θ). The policy gradient theorem provides a foundation for this:

$\nabla_\theta j\left(\pi_\theta\right)=\mathbb{E}_{s, a \sim \pi_\theta}\left[\nabla_\theta \log \pi_\theta(a \mid s) \varrho^{\pi_0}(s, a)\right.$        (14)

This principle underlies modern algorithms such as REINFORCE, PPO, and A3C ‎[71].

In recent years, DRL processes have undergone tremendous growth, mainly with respect to sample efficiency, stability, and scaling aspects. For example, off-policy techniques like SAC employ entropy regularization to reward exploration and pre- vent premature convergence ‎[72-74]. On the other hand, model-based reinforcement learning types try to learn dynamics in order to limit the need for expensive real interactions in the real world ‎[75]. Meta-reinforcement learning and multi-agent reinforcement learning have also been studied for real applications requiring adaptability and cooperation ‎[76].

Reinforcement learning has been used extensively in robotics control (autonomous drones, manipulators), game playing (AlphaGo, AlphaStar), and sequential decision systems in recommendation engines, autonomous driving, and energy management. In agriculture, reinforcement learning has been employed for irrigation scheduling, greenhouse climate control, and resource allocation in dynamic environments where the reward signifies productivity or sustainability ‎[77].

Despite its success, reinforcement learning faces several challenges when applied to real-life domains, such as smart agriculture. A major problem here is the exploration exploitation dilemma, where the agent must decide between trying new strategies to see if better payoffs can be procured and exploiting known rewarding strategies. Most reinforcement learning methods, however, tend to be sample-inefficient, meaning that they take a very long while interacting with the environment before they can provide stable performance. This is usually exacerbated by the high variance in the gradient estimates of policy optimization algorithms, making them unstable and hard to scale into complex agricultural systems.

5.3 Overfitting

It arises when a model learns noise or idiosyncratic quirks of the training set rather than the underlying distribution, thereby undermining its performance on fresh data.

5.3.1 Susceptibility of small or low-dimensional datasets

Charilaou and Battat emphasize how machine-learning models, while mostly outperforming classical regression, must nonetheless be rigorously validated, lest they over-fit on small clinical datasets (e.g., n = 146) where “naive” AUCs (trained on all data) substantially exceed cross-validated AUCs; this is evidence of over-fitting ‎[78].

Subramanian and Simon provided simulation evidence for serious over-fitting of low-dimensional data (p ¡ n) due to weak predictor outcome relationships, advocating for either a separate test set or complete cross-validation regardless of dimensionality

5.3.2 Hyper-parameter tuning and nested cross-validation

When tuning model hyper-parameters such as network depth and regularization strength, performing k-fold cross-validation both to select hyper-parameters and to evaluate performance can itself induce bias. Charilaou and Battat suggest a nested k-fold design to remedy this situation, with an inner loop for hyperparameter search and an outer loop for final performance estimation, thus preventing optimistic performance inflation.

5.3.3 Events-per-variable considerations

In classification tasks with binary outcomes, the rule of thumb of at least ten events per predictor helps guard against over-fitting in logistic models. In small data settings, both the number of folds and stratification by outcome prevalence must be chosen carefully to ensure each training fold contains sufficient positive events. Table 6 contrasts underfitting and overfitting, the two error regimes that careful validation must guard against.

Table 6. Comparison of underfitting and overfitting ‎[78, 79]

5.4 Generalization

Generalization measures how well a model trained on one dataset performs on truly new data, reflecting its real-world utility.

5.4.1 Overconfidence and under-performance pitfalls

Aliferis and Simon survey scenarios in which overconfident models, those exhibiting high apparent accuracy on validation sets fail on new data. They outline best practices (e.g., robust validation, calibration techniques) to prevent and correct over confidence and under-confidence, underscoring that generalization is not guaranteed simply by model complexity control ‎[80].

5.4.2 Class-dependent effects of regularization

Regularization curbs the overfitting discussed in Section 5.3, but its constraints are usually tuned to maximize average accuracy and therefore do not affect every class equally often eroding performance on the under-represented classes, which in greenhouse datasets tend to be the diseased or nutrient-deficient plants of greatest interest. Table 7 summarizes common regularization techniques and how their effects vary by class.

Table 7. Regularization techniques and their effects ‎[80, 81]

Balestriero, Bottou, and LeCun reveal that standard regularizes like data augmentation (random crops) or weight decay, tuned via cross-validation to maximize average accuracy, can disproportionately harm performance on specific classes. For example, when training a ResNet-50 on ImageNet, introducing random-crop augmentation reduces” barn spider” test accuracy from 68% to 46%, illustrating that naive regularization can introduce hidden biases in generalization ‎[79].

5.5 Cross-validation

Cross-validation refers to performing several train/test splits in order to provide more reliable estimates at different levels of confidence regarding out-of-sample prediction performance and to guard against over-fitting.

5.5.1 Power and confidence issues in nested k-fold

Ghasemzadeh et al. [79] tested four kinds of validation methods, single holdout, k-fold, train-validation-test, and nested k-fold, on clinical voice data. Nested 10-fold cross-validation produces the highest statistical confidence (likelihood of selecting the correct features) and an unbiased estimate of accuracy; on the other hand, single-holdout models grossly overestimate performance and need maybe 50% more samples to attain the same power. Nested k-fold might yield an improvement of nearly 4 times in confidence over simpler splits ‎[82].

5.5.2 Spatial and domain transferability

Habibi, Matsui, and Tanaka evaluate random CV, spatial CV (cluster-based), and leave-one-field-out CV for UAV-based soy- bean yield prediction. Random CV fails to capture variability beyond the training domain, while the spatially aware CV strategies are better candidates for extrapolating to new fields. Moreover, applying simple models (LASSO with recursive feature elimination) further enhances transferability, recommending spatial CV as a standard for robust, real-world yield forecasting ‎[81].

5.5.3 K-Fold for transformer-based symbolic regression

‎Implements 5-fold cross-validation on a transformer- based symbolic regression model trained on only 15,000 (vs. 500,000) synthetic data points. K-fold CV reduces over-fitting, improving validation loss by 53.31% relative to a standard 80/20 train/test split, and yields performance comparable to a model trained on a much larger dataset. Such findings give evidence that K-fold CV is significant for the generalization of models in data-poor environments. Figure 9 illustrates the difference between appropriate fitting and underfitting.

9.png

Figure 9. Appropriate fitting vs. underfitting illustration

Along the sensing modality axis, we contrast RGB, multispectral, and thermal imaging by what each measure, what it cannot, and its cost/robustness trade-offs.

7.1 Multispectral analysis

Multispectral imaging has emerged as a cornerstone in modern smart greenhouse systems, providing insights into plants physiology beyond what traditional RGB cameras can capture. While RGB imaging is akin to reading only the cover of a book, multispectral imaging lets researchers explore multiple internal chapters-each spectral band revealing a different physiological characteristic of the plant.

7.1.1 RGB Imaging vs multispectral imaging

RGB imaging utilizes three broad visible spectral bands: red (approximately 620–700 nm), green (495–570 nm), and blue (450–495 nm). It effectively captures color, structure and visible stress indicators such as chlorosis or wilting, all at relatively low cost and high spatial resolution. Multispectral imaging, by contrast, includes discrete non-visible bands such as Near-Infrared (NIR), Red Edge, or Short-Wave Infrared (SWIR), which reveal subtle spectral signatures linked to chlorophyll absorption, water content, and leaf cell structure. These additional bands enable computation of various vegetation indices that can detect crop stress nutrient deficiency, or disease before visible symptoms appear.

7.1.2 Key vegetation indices and metrics

Table 9 gathers the key vegetation indices computed from multispectral data:

•NDVI (Normalized Difference Vegetation Index): used worldwide as a proxy for greenness, biomass, and plant vigor.

•SAVI (Soil Adjusted Vegetation Index): Adjusts NDVI to reduce the effect of soil brightness.

•Red Edge Indices: Sensitive to changes due to early stress and variation in chlorophyll.

Table 9. Key vegetation indices

Other RGB-based indices, like Red Edge Normalized Difference Vegetation Index (RENDVI) and Visible Atmospherically Resistant Index (VARI), are used for estimating vegetation health, but their performance is commonly limited under low contrast or early-stress conditions.

7.1.3 Areas of application in smart greenhouses

Multispectral imaging is considered better than alternatives for applications like plant health monitoring where it detects early nutrient deficiency and disease from red-edge and near infrared bands, water stress assessment NIR and red-edge reflectance strongly correlate with leaf water content for precision irrigation and canopy and biomass estimation where it facilitates canopy cover and leaf area index (LAI) measurements under variable illumination conditions more accurately.

For example, ‎Burchard-Levine et al. [93] presented a multispectral 3D point-cloud approach which could measure the chlorophyll content for greenhouse tomatoes more accurately than 2D RGB methods; these estimations strongly correlated with SPAD meter readings. Likewise, ‎Poirier-Pocovi et al. [94] also compared UAV-based RGB and multispectral indices for palm cultivation and reported that the multispectral system was more reliable under changing illumination.

7.1.4 Advantages and limitations of multispectral imaging

Multispectral imaging offers several advantages for smart greenhouse monitoring: by capturing reflectance in non-visible bands such as near infrared and red edge, it detects nutrient deficiency, water stress, and disease before symptoms become visible to RGB sensors, and it enables quantitative vegetation indices (NDVI, SAVI, red-edge) that correlate with chlorophyll content, biomass, and leaf water status. It is also non-destructive and scales from handheld to UAV and fixed-mount deployment. Its limitations, however, constrain low-cost adoption: multispectral cameras are markedly more expensive than RGB, their readings are sensitive to illumination and require radiometric calibration and, in field use, atmospheric correction, and the vegetation indices can saturate at high biomass or lose sensitivity under early or low-contrast stress. As with thermal imaging, the most robust results reported in the literature come from fusing multispectral data with RGB or thermal modalities rather than relying on any single sensor.

7.1.5 Integration and emerging trends

Emerging techniques focus on the fusion of RGB, multispectral, and thermal imaging for better accuracy. Integrating them into UAVs, fixed sensor networks, and roof-mounted greenhouse systems is becoming common. Machine Learning models such as Random Forests, Support Vector Regression, and Deep Neural Networks are increasingly employed to estimate chlorophyll content, biomass, and stress indicators using multispectral data.

7.2 Thermal imaging

Whereas multispectral imaging works with reflected radiation, thermal imaging works with infrared radiation, which is being emitted by plant surfaces and carries information on physiological processes that cannot be recorded by optical sensors.

Thermal imageries are emerging as a backbone to smart greenhouse systems to observe the physiological behavior of plants non-invasively contrary to RGB or multi-spectral imagers, which use reflected visible or near-infrared light, the thermal imagers record the emitted infrared radiation from plant surfaces in the wavelength of 8–14 µm. Thermal maps are considered a direct abstraction of insights regarding the transpiration rate, stomatal conductance, and water stress situations of crops ‎[95].

In a conventional setting, the thermal camera views radio- metric temperature of leaves, which is in large measure directly correlated to water status in the plant. Plants undergo water deficit where the stomata close and transpiration gets reduced, in which case the leaf becomes warmer. This phenomenon forms the core behind indices like Crop Water Stress Index (CWSI), which is calculated from canopy temperature relative to reference temperature ‎[96].

$C W S I=\frac{T_c-T_{w e t}}{T_{d r y}-T_{w e t}}$       (21)

where, $T_c$ is the canopy temperature, and $T_{\text {wet}}$ and $T_{d r y}$ are the reference temperatures for fully transpiring and non-transpiring leaves, respectively.

Thermal imaging can be applied for early stress detection, irrigation scheduling, and automated climate control in green- houses. For example, the study ‎[97] has shown that coupling thermal sensors with IoT-based systems allows for the forming of real-time feedback loops to optimize irrigation and ventilation. Similarly, the combination of thermal and multispectral data improves the accuracy of stress detection and plant growth modeling ‎[98]. Table 10 compares the RGB, multispectral, and thermal sensing modalities.

Cooling down the effects caused by field variations and camera calibration problems along with the background radiation often create matters for thermal imaging ‎[99]. Future trends involve integrating thermal data with AI-based analytics and sensor fusion techniques for enhancing the smart greenhouse management decision-making.

Table 10. Modality comparison table

7.2.1 Advantages and limitations of thermal imaging

Thermal imaging offers several advantages for smart greenhouse management: it enables non-invasive monitoring of plant physiological status by measuring emitted infrared radiation, providing real-time feedback on transpiration rate, stomatal conductance, and water stress, and, unlike optical imaging, it operates independently of ambient lighting to maintain consistent measurements from day to night; furthermore, thermal data can be integrated with IoT and AI-based systems for automated irrigation scheduling, early stress detection, and precise climate control, and when combined with multispectral imaging it supports far more accurate crop stress diagnosis and growth modeling. However, the technology also has notable limitations: it is highly sensitive to environmental variations such as wind, humidity, and background radiation that distort temperature readings. It depends on critical yet complicated camera calibration and emissivity correction to ensure accuracy; the high cost of thermal sensors and the intricate data-processing algorithms they require limit large-scale adoption in low-cost commercial greenhouses; and thermal imaging alone typically cannot distinguish between sources of temperature variation. For example, whether a change stems from disease or water deficit requires the combination with other sensing technologies for robust decision-making.

In this coming decade, smart greenhouse systems will experience tremendous changes due to the rapid development of sensing technologies, IoT architectures, and artificial intelligence. Chief among the proposed developmental trajectories is the enhanced path towards AI-driven agricultural intelligence and the interference of edge technology with digital twin frameworks.

9.1 Trends in AI driven agriculture

Agricultural AI systems are expected to transition from single-task applications such as disease detection or yield estimation toward multi-task learning architectures capable of simultaneously managing multiple greenhouse control processes. These models will integrate heterogeneous data streams, including visual, climatic, and soil measurements, to coordinate irrigation, climate regulation, and pest management in a unified framework.

Recent advances in few-shot learning and federated learning are likely to address two major limitations of current agricultural AI systems: the scarcity of labeled datasets and concerns related to data privacy among stakeholders. Federated learning, in particular, enables collaborative model training across multiple greenhouse sites without requiring the exchange of raw data. In parallel, the integration of AI with autonomous robotic platforms such as mobile units for precision spraying, automated harvesting, and plant inspection is gaining increasing attention. These continuously operating systems not only enhance operational efficiency but also generate additional data streams that further enrich AI-driven decision-making.

9.2 Edge-computing and digital-twins perspective

Cloud-centric greenhouse control architectures are often constrained by latency, bandwidth requirements, and intermittent connectivity, especially in rural or remote agricultural environments. Edge computing addresses these limitations by enabling local processing of sensor data on embedded devices, thereby supporting real-time control even in the absence of continuous internet connectivity. Edge AI accelerators, such as NVIDIA Jetson and Google Coral platforms, allow inference tasks such as image-based disease recognition or short-term climate prediction to be executed directly within the greenhouse environment.

In parallel, digital twin technology introduces a virtual representation of the physical greenhouse that remains synchronized with real-time sensor data. This virtual counterpart enables the simulation and evaluation of control strategies before physical deployment, reducing the risk of crop stress and operational failures. Digital twins further support yield optimization, resource and energy efficiency analysis, and “what-if” scenario evaluation under climate anomalies ‎[103].

The combination of edge computing and digital twins facilitates hybrid architectures in which high-frequency sensor data and real-time simulations are processed locally, while cloud platforms are leveraged periodically for long-term analytics, data storage, and collaborative model improvement. Collectively, these developments point toward the emergence of self-optimizing greenhouse systems that operate with minimal human intervention while maximizing productivity and resource efficiency.

The authors gratefully acknowledge the full financial support provided by BI Engineering Tech. Their contribution was essential in enabling the completion of this review.