Monitoring and Control in Solar Drying Systems: A Review of Sensors, Control Strategies and Digital Architectures

1. Introduction

Solar drying offers a viable solution for preserving agricultural products and addressing global challenges [1]. Also, contributes to food security by reducing post-harvest losses, promotes sustainability by depending on renewable energy, and stimulates economic development in rural areas by adding value to agricultural products [2,3]. However, the operation of solar drying systems is strongly influenced by how the process is monitored and controlled. The lack of control in operating conditions can lead to prolonged drying times, poor-quality product, and inefficient energy use [4,5]. Despite these challenges, few studies have integrated both control and monitoring aspects in a single synthesis, resulting in a gap in the literature that this paper aims to fill.

The use of solar energy for food drying dates back centuries, with traditional open-sun drying [6]. This inexpensive method is the simplest and most commonly used for drying, and involves placing the food and exposing it to air and sunlight. However, this method is slow, weather-dependent, and exposes the product to pests, dust, and other contaminants [6].

On the other hand, the commercialization of dehydrated products is affected by variability in quality, often due to the lack of systematic monitoring and control of drying conditions and consequently, a wide variety of solar drying systems (SDS) have been developed, ranging from simple direct dryers (where products are enclosed and directly exposed to sunlight) to more complex indirect or mixed-mode setups that use solar collectors to heat the air, then circulated through the drying chamber [7,8].

Hybrid dryers that integrate solar energy with conventional sources, such as electricity, have been proposed to enhance reliability during variations in solar irradiance [9]. The dryer’s design depends on the type of product, climate, and economic constraints [7,8,9], and its efficiency is influenced by how well key process variables are measured and, when feasible, actively controlled. Therefore, the control techniques are employed to maintain these optimal drying conditions, specifically air temperature, humidity, and airflow rate, which directly influence product temperature and moisture content [10,11]. This control implementation range from simple rule-based or on-off strategies (such as manual or automatic vent opening and fan switching) to closed-loop control using PID algorithms or fuzzy logic, and, more recently, intelligent, data-driven approaches [10,11]. On the other side, monitoring techniques enhance these controllers by providing real-time information about the process state, typically through measurements of air temperature and humidity, product moisture content, and, in some cases, product weight loss over time [12]. Also, the sensor’s quality, placement, data resolution, information storage, and analysis directly affect system efficiency and performance assessment.

As a result, recent studies highlight that integrating control and monitoring techniques into industrial-scale drying systems is a significant challenge [13,14], due the variability in climatic conditions, the distributed nature of solar collectors and drying chambers, and constraints on cost and maintenance in rural settings complicate the design of robust control architectures [13,14]. However, advances in digital sensors, low-cost microcontrollers, and Internet of Things (IoT) platforms have opened new possibilities for real-time data acquisition, remote supervision, and automated decision-making in solar dryers [15,16]. The economic viability of these solutions is essential, particularly in areas that have limited access to conventional energy sources. Therefore, it is necessary to adopt integrated approaches that lower the costs of sensors and controls, optimize energy usage, and clearly demonstrate a favorable cost-benefit ratio [17,18].

Although control and monitoring play an important role in solar drying applications, many existing reviews focus primarily on dryer configurations, thermal storage integration, or product quality outcomes. In contrast, topics such as instrumentation, automation, and closed-loop control are often viewed as secondary or relegated to future work [26], resulting in a lack of comprehensive, technically detailed syntheses dedicated to monitoring and control—especially regarding recent advancements in smart sensing, connected devices, and data-driven methods.

This paper addresses this gap by providing a comprehensive review of control and monitoring techniques for solar drying applications, with an emphasis on recent advancements. Through a structured examination of the latest research, the review explores various control strategies—including traditional rule-based and PID controllers, and more advanced methods such as fuzzy logic and networked control systems—as well as automatic monitoring systems and IoT-enabled solutions.

The review centers on the process variables usually measured and controlled, examining how these choices affect drying time, product quality, and energy efficiency. Finally, the paper identifies limitations, emerging research opportunities, and practical recommendations to guide the development of robust and economically viable control architectures for solar drying systems.

Solar Drying Review View

In the last decade, solar drying has been extensively reviewed regarding dryer types and configurations [19,20,21], system-level performance and scale-up [8,20,22], and modeling and product quality considerations [23,24,25]. Monitoring and control are frequently regarded only as supporting elements or as tasks for future consideration (e.g., Saini et al. [26] and C. K. Saha et al. [27]), rather than as the main design layer required for consistent performance under varying solar input.

Table 1 outlines the key review themes and illustrates how this review enhances them by concentrating on the measurement, actuation, and digital control architectures for solar dryers. Table 1 is intended as an illustrative thematic map rather than a systematic classification: the review papers were identified through a parallel search filtered to document type = review, and each review was assigned to the theme matching its primary focus (with secondary themes noted where applicable). A supplementary list of the review papers and their assigned themes is provided. Within the “monitoring, control, and digitalization” row, some cited works are drawn from adjacent industrial solar-heat and process-control literature rather than from solar-drying reviews specifically; these are included deliberately to triangulate the identified gap across both the solar-dryer and the broader drying-control literatures.

2. Review Methodology

2.1. Search Strategy

The review was conducted following a structured search strategy in major scientific databases, primarily Scopus and Web of Science, complemented by targeted searches in Google Scholar to capture additional relevant references. The search focused on the period 2000–2025, when most of the reported work on modern solar drying systems and digital control and monitoring solutions has been published. Combinations of the following keywords were used in titles, abstracts and author keywords: “solar drying”, “solar dryer”, “solar drying system”, “solar dehydrator”, “solar thermal”, together with “control”, “automatic control”, “process control”, “monitoring”, “data acquisition”, “PID”, “fuzzy”, “networked control”, “IoT”, “wireless”, and “smart sensor”. Only documents written in English and published as journal articles or peer–reviewed conference papers were considered. This method follows established guidance for conducting structured literature reviews in interdisciplinary engineering and energy research [37,38,39]. Anthropic Claude AI assisted with initial article screening, study classification, language editing, and figure scripting; all outputs were verified by the authors, who take full responsibility for the content of this publication.

2.2. Inclusion and Exclusion Criteria

The initial search returned a broad set of publications related to solar energy use in drying, as well as general work on solar thermal systems. To focus the review on control and monitoring aspects, the following inclusion criteria were applied:

(1)

the study deals with solar thermal drying systems or with solar thermal plants that supply low–temperature heat to drying or similar processes;

(2)

the paper reports an explicit control or monitoring strategy, or presents an experimental or numerical study where control-relevant variables (e.g., air temperature, relative humidity, air flow rate, product moisture) are measured and used to assess system performance;

(3)

sufficient technical details are provided on the control or monitoring hardware (sensors, actuators, controllers, communication modules) and/or on the control algorithms implemented.

Studies were excluded if they: (1) focused exclusively on dryer design, modelling or drying kinetics without any discussion of control or monitoring; (2) dealt with non-solar drying technologies (e.g., purely electric or fossil-fuel dryers) without a clear link to solar-assisted configurations; (3) were purely economic, social or policy analyses without technical description of control or monitoring systems; or (4) consisted of patents, theses or non peer–reviewed reports.

Explicit inclusion/exclusion criteria were defined to ensure transparency, reproducibility, and a focused synthesis [38,39].

2.3. Data Extraction and Classification

For each selected paper, the following information was extracted: type of solar drying system (distributed, integral or mixed; passive, active or hybrid), type of product and operating scale (laboratory, pilot or field), process variables measured and/or controlled (e.g., air temperature, relative humidity, air flow rate, product temperature or moisture), control and monitoring approach (rule-based control, on–off switching, PID control, fuzzy or other advanced controllers, automatic monitoring systems, IoT–based solutions, smart sensors), hardware platform (microcontrollers, PLCs, data loggers, communication modules) and performance indicators (drying time, final moisture content, quality attributes, energy indicators such as solar fraction, thermal efficiency or specific moisture extraction rate).

The study identification and selection process followed a structured protocol in line with PRISMA guidelines [40]. Figure 1 illustrates the flow of information through the review’s phases. A total of 492 records were initially retrieved from Scopus, Web of Science, and Google Scholar. After removing duplicates and screening titles and abstracts, 130 full-text articles were reviewed; 78 met the inclusion criteria and were included in the final analysis.

Duplicate records were identified and removed. Of the 130 full-text articles assessed for eligibility, 52 were excluded; the reasons for exclusion (no explicit control or monitoring strategy; non-solar drying technology without a solar-assisted link; purely economic, social, or policy scope; insufficient technical detail; or non-peer-reviewed source) are summarized with counts in the Supplementary Material, together with a completed PRISMA 2020 checklist.

The studies were categorized into broad groups based on the following criteria: (a) the configuration of the solar drying system (distributed, integral, or mixed), (b) the primary control and monitoring strategies used, and (c) the complexity and intended application (small-scale rural dryers, pilot-scale demonstrators, industrial applications, or solar heating for industry (SHIP)). This classification will be used in subsequent sections to organize the discussion of control and monitoring techniques and to highlight current trends, gaps, and research opportunities.

3. Background

This section offers an overview of the current state of solar drying technologies. It includes a discussion of the various types of solar drying systems, the control and monitoring techniques currently employed, and the existing limitations and challenges in this field. This background will lay the groundwork for a more detailed exploration of control and monitoring techniques for solar drying applications in the following sections. In the broader solar drying literature, control and monitoring are frequently identified as enabling technologies for performance, quality, and reliability. Nevertheless, they are rarely discussed with the same level of technical depth as thermal design and performance evaluation, which motivates the focused coverage that follows.

3.1. Classification of Solar Drying Systems

Solar drying can be performed either through traditional open-sun drying or through more technical Solar Drying Systems (SDS). In open sun drying, the product is directly exposed to ambient air and solar radiation, which makes the process simple and low-cost but strongly dependent on weather conditions and vulnerable to contamination by dust, insects, and animals [6,41]. In contrast, SDS provides an enclosed environment in which solar energy is captured and transferred to the drying air in a more controlled way, improving hygiene, product quality, and process reproducibility [7,21,42,43].

SDS are commonly classified according to the airflow driving mode as passive (natural convection) or active (forced convection), and according to the relative arrangement of the collector and drying chamber as distributed, integral, or mixed systems [7,42,44]. In passive dryers, buoyancy forces driven by temperature gradients generate airflow, simplifying construction and operation but limiting control of airflow velocity and temperature. Active dryers use fans or blowers, typically powered by electricity or photovoltaics, to impose a prescribed airflow rate, offering greater flexibility for process control and sensor and actuator integration.

In distributed solar dryers, the solar collector and the drying chamber are separate units. Incident solar radiation is converted to heat in the collector and transferred to a working fluid (usually air or water) that subsequently delivers heat to the product inside the drying chamber [7,42,43]. When air is used, these systems are generally referred to as direct air-heating solar drying systems; when a liquid is used as intermediate fluid, they constitute indirect air-heating systems, often coupled with storage to attenuate solar intermittency [42,45,46]. In both cases, the solar air heater or solar thermal collector is a key component whose design and performance directly influence the controllability of the drying temperature and airflow.

Integral solar dryers combine the functions of solar energy capture and product containment in a single unit, so that the product is located inside a chamber that also acts as a solar collector, typically with one or more transparent covers [7,42]. In these systems, solar radiation penetrates the transparent surface and is absorbed by the internal surfaces and the product itself, while the cover reduces convective losses due to air. This configuration increases the drying chamber temperature compared to open sun drying, but at the cost of reduced flexibility to independently adjust collector and chamber conditions.

Mixed solar dryers combine features of distributed and integral configurations. A portion of the solar energy is absorbed by a dedicated collector and transferred to the airflow, while the product continues to receive direct or diffuse solar radiation through transparent surfaces [47,48,49]. Mixed designs aim to exploit the advantages of both concepts: improved energy capture and higher controllability of air temperature and flow, while maintaining relatively simple construction and the possibility of integrating thermal storage or auxiliary heaters. The choice of configuration (distributed, integral, or mixed; passive or active) has direct implications for what can be effectively monitored and controlled: in distributed and active systems, separate access to collector and chamber variables facilitates advanced control of temperature, humidity, and airflow, whereas integral and passive systems tend to rely on simpler monitoring strategies focused on chamber temperature and relative humidity, often with limited actuation options [50,51,52].

To relate this taxonomy to the reviewed corpus and to support the claims about controllability, the distribution of the 78 included studies across configuration classes is summarized in the Supplementary Material. For readers more familiar with the conventional direct/indirect/mixed-mode typology, the present framing maps approximately as follows: integral (greenhouse) dryers correspond to direct mode, in which solar radiation reaches the product directly; distributed dryers correspond to indirect mode, in which a separate collector heats the working fluid that is then delivered to the chamber; and mixed designs combine both. The terms “direct” and “indirect” air heating are used here to distinguish air heated directly in a solar air heater from air heated through a liquid-mediated loop, which is a separate distinction from whether radiation reaches the product. Finally, because the contribution of this review is control- and monitoring-oriented, the configuration framework should be read together with the secondary, control-relevant degrees of freedom that determine the available actuators and measurable indicators—namely recirculation versus once-through ventilation, the presence of thermal storage, auxiliary heaters, PV-powered fans, and dehumidification—which are summarized alongside Figure 2.

3.1.1. Principles of Thermal Drying

The drying of biological materials is a complex process governed by simultaneous heat and mass transfer mechanisms. Heat is transferred from the surrounding air to the product’s surface, while moisture migrates from the interior to the surface, where it evaporates into the airflow. These coupled phenomena are influenced by both internal resistance (within the solid matrix) and external resistance (at the product–air interface) [53]. Two transport mechanisms simultaneously occur during drying: (i) heat transfer from the medium to the wet material, inducing changes from water to the vapor phase, and (ii) water transport, liquid or vapor, from the inside to the surface, where moisture is removed by gas. Therefore, there are two resistances to moisture removal (internal and external). Heat and mass transfer phenomena occur simultaneously both externally and internally within the solid matrix and are generally influenced by differences in temperature and water concentration, the air velocity field, and the material’s properties. A conceptual representation of the thermal drying process is shown in Figure 3.

In hygroscopic materials, such as agricultural products, the drying process follows two principal regimes: the constant-rate period and the falling-rate period [44,54]. During the constant-rate period, surface moisture is removed rapidly, and the drying rate is governed primarily by external conditions, including air temperature, humidity, and airflow velocity. Moisture migrates easily to the surface, resulting in minimal internal resistance [44].

As drying progresses, the critical moisture content is reached (the point at which the rate of internal moisture migration no longer keeps pace with surface evaporation). This marks the onset of the falling-rate period, during which internal resistance dominates, and moisture transport slows as migration through the dry outer layers becomes increasingly complex [55]. The falling-rate stage is crucial for control applications, as improper regulation of temperature or airflow during this phase can result in over-drying, quality loss, or increased energy consumption [54]. Effective drying strategies must account for material properties, temperature, moisture gradients, and dynamic air properties, all of which influence the simultaneous internal and external transfer processes.

These principles apply universally to solar drying systems and underpin the design of adaptive control methods. Figure 3 illustrates a conceptual representation of these coupled mechanisms in thermal drying [44,54].

Because the smart-sensing and moisture-estimation methods discussed later in this review rely on psychrometric relationships, it is useful to introduce the relevant quantities here. The state of moist air is more completely described by the humidity ratio (or absolute humidity) W, defined as the mass of water vapor per unit mass of dry air, than by relative humidity alone: at a fixed humidity ratio, relative humidity falls as air temperature rises, so relative humidity by itself is not a sufficient control variable across varying temperatures. The moist-air enthalpy, which combines the sensible heat of the air and the latent heat carried by the water vapor, governs the energy available for evaporation. For a dryer operating at steady airflow, a simple mass balance on the air stream links the water removal rate to the difference between the outlet and inlet humidity ratios: the rate of water removed equals the dry-air mass flow rate multiplied by the increase in humidity ratio across the chamber. This relationship provides the physical basis for estimating drying rate and product moisture from paired temperature and relative-humidity measurements at the inlet and outlet, and it motivates the use of humidity-ratio trends—rather than relative humidity alone—as monitoring and control indicators.

3.1.2. Drying Air Characteristics

The quality characteristics (color, flavor, texture, nutraceutical components, etc.) of the dehydrated product determine the guidelines for selecting drying operating parameters: temperature, relative humidity, and air velocity. Therefore, it is important to identify operating parameters that ensure the desired product quality while also optimizing energy use and drying time, which are closely linked to the economic feasibility of the drying process.

The relative humidity of the drying air is crucial for the drying process. The air’s moisture-retention capacity can be increased by dehumidifying or heating the air before it enters the drying chamber, thereby increasing its evaporation capacity [56]. Furthermore, in certain products, relative air humidity at a certain level (25%) prevents surface hardening and crusting on the surface of the solid [57]; in such a way that it prevents the drying speed from decreasing.

Due to solar intermittency, solar-powered technologies typically operate in batch mode for a few hours a day or more than a day, especially for whole (non-sliced) vegetables. In batch drying, the drying chamber is loaded with a fixed amount of wet material at the beginning of the process, and the “dry” material with the desired humidity is discharged at the end. In the drying of hygroscopic products, such as crops and foods, the rate of moisture loss is most significant at the beginning of the drying process, when the moisture content is high, and decreases dramatically as the moisture content approaches zero at the end of the drying process. Therefore, in a batch process, the relative humidity of the air inside the drying chamber at the beginning is much higher than at the end. Consequently, at the beginning of the drying process, it is important to remove the humid air by extracting it from the drying chamber, allowing the entry of new air with lower relative humidity and greater capacity to absorb water from the drying chamber’s wet products. As a practical guideline reported for conventional hot-air dryers operating with a constant inlet airflow, the relative humidity at the drying-chamber outlet is generally kept below approximately 60% to preserve the moisture-carrying capacity of the exhaust air. This value should be treated as context-dependent (product class, air temperature, and drying stage) rather than as a fixed threshold.

3.1.3. Airflow Rate and Air Velocity

Airflow rate is another important parameter that influences the drying process. Insufficient airflow can lead to slow moisture removal and high drying temperatures. Air velocity significantly affects the drying rate. Higher air velocity reduces the thickness of the stagnant air film on the product surface, thereby decreasing the resistance to both moisture diffusion and heat transfer from the hot air to the product.

The internal resistance to moisture movement in agricultural products is much greater than the resistance to surface mass transfer, as airflow rates above certain levels do not significantly affect the drying rate. Therefore, a higher flow can be used at the beginning of drying, and a lower flow can be used during the falling-rate period [56,58].

In batch operation, the temperature or velocity of the drying air can be varied over time to match the requirements of instantaneous drying kinetics [59]. At the beginning of drying, maximum airflow at a low temperature is used because the product surface is saturated with water and moisture removal is directly related to the mass flow of air. Later, during the falling-rate period, lower airflow is used while the temperature is increased—up to a limit determined by the product’s thermal sensitivity—to enhance the diffusion rate of internal moisture at the end of the drying process. This operational strategy has already been reported for batch drying of black ginger [60] and rubber sheets [61]. Through this intermittent batch drying approach, drying time and energy consumption were reduced, thereby increasing the overall efficiency of the drying system [60], at the expense of decreasing the efficiency of the SAH system [61].

3.1.4. Drying Air Temperature

In thermal drying with hot air, a key control parameter is the product temperature, as it affects quality characteristics. Although higher drying temperatures speed up the drying process, they could also damage the product (loss of color, flavor, aroma, and vitamins) [56].

Increasing the temperature of the drying air will increase the drying rate in two ways. Firstly, this increases the drying air’s ability to retain moisture. Second, the heated air will raise the product’s vapor pressure. This phenomenon will drive moisture to the surface faster. However, there is a limit to how high the air temperature in a dryer can be raised. The thermal sensitivity of most fruits and vegetables often limits the operation of high-temperature dryers. If the temperature is too high at first, a hard layer (dry layer) can develop on the outside (known as surface hardening) that traps moisture within the layer. High temperatures at the end of the drying period can cause food to burn.

The product’s temperature will always be lower at the beginning of drying than at the end. At the beginning of the process, the energy supplied primarily goes into evaporating the water removed from the product (latent heat of vaporization). In contrast, at the end of the process, the energy supplied is practically invested in increasing the solid product’s sensible energy. There is a maximum permissible temperature for the product that maintains the quality characteristics desired by consumers. However, it must also be kept in mind that there is a minimum drying time under certain conditions (temperature and relative humidity) to guarantee quality characteristics. Drying in minimum time can be achieved by continuous drying.

Effective control and monitoring of solar drying systems are critical for optimizing and improving the quality of dried products. These systems involve interactions between solar collectors, the drying chambers, and control components. Consequently, implementing effective control and monitoring techniques is necessary to manage these interactions and obtain the required results. One critical aspect of managing solar drying systems is regulating airflow. Forced convection, often driven by fans or blowers, is commonly used to increase drying rates and ensure uniformity. The regulation of airflow, temperature, and humidity inside the dryer is typically achieved through feedback control systems that adjust fan or blower operation based on conditions within the drying chamber [62]. Monitoring techniques, alongside control techniques, are essential for the effective operation of solar drying systems. These techniques use sensors to measure key parameters, as temperature, humidity or solar radiation. The data gathered by these sensors is then utilized to guide control decisions and evaluate the system’s performance. Recent advancements in sensor technology and data analytics have allowed the development of more advanced monitoring techniques, for example, the real-time monitoring and predictive modeling [53,63,64]. Despite significant advancements in solar drying technologies, several limitations and challenges remain. These include difficulties in controlling the drying process, the impact of external and internal factors on the drying rate, and the need for robust, low-cost, intelligent sensors for effective monitoring and control. Traditional open-sun-drying methods rely heavily on external factors, such as solar radiation, ambient temperature, air speed, and relative humidity. They are also influenced by internal factors, as the initial moisture content, the product type, and the exposed mass. This dependency makes a difficult to control of the drying process, resulting in inconsistent product quality, specially in food products.

While solar dryers can reduce drying time, they too have limitations. For example, the air temperature at the outlet of a solar collector in a solar dryer can fluctuate significantly, affecting both the drying duration and the quality of the final dried product. [65]. Moreover, traditional drying methods, such as open-sun drying and conventional fuel dryers, are often unsuitable due to issues like contamination, rodent and insect infestations, fungal growth, and rising drying costs [66].

4. Control and Monitoring Techniques for Solar Drying

The efficiency, accuracy, and reliability of solar drying systems are influenced not only by the thermal design of collectors and chambers but also by the monitoring and control of key process variables. Solar radiation is naturally intermittent, and both ambient conditions and product characteristics can change throughout the drying cycle. As a result, recent advancements in solar drying technologies have increasingly focused on integrating sensors, microcontrollers, and communication technologies. These tools are used to measure important state variables and implement control strategies that maintain appropriate temperature, humidity, and airflow profiles for specific products and dryer configurations [42,57,66,67].

This section summarizes recent advancements in control and monitoring techniques, highlighting the measured variables, actuators used, and reported performance indicators.

4.1. Rule-Based and Pid Control in Solar Dryers

The efficiency, accuracy, and reliability of solar drying systems are heavily influenced by effective control and monitoring. This section summarizes recent advancements in these techniques.

Many practical solar dryers still use simple rule-based strategies, such as on-off control of fans, dampers, or auxiliary heaters, based on threshold values for chamber temperature or relative humidity. These strategies are appealing for small-scale applications because they are easy to implement, require minimal computational resources, and can be executed with inexpensive microcontrollers [53,67]. However, using simple on-off control often results in temperature and humidity oscillations and may struggle to adjust to rapid changes in solar irradiance.

More advanced systems utilize PID (Proportional–Integral–Derivative) algorithms to effectively regulate the air temperature within the drying chamber, as demonstrated in the research by Herrera-Morales et al. [32], a solar dryer for potato drying incorporates an electronic PID controller that adjusts the operation of fans and an electric resistance heater to maintain the chamber temperature close to a specified setpoint. The control system measures the air temperature and relative humidity inside the dryer and modulates the actuation to reduce overshoot and shorten settling time, resulting in a more uniform temperature profile and a more predictable drying time. This kind of feedback control is particularly relevant in hybrid solar–electric dryers, where auxiliary energy can be used efficiently only if the control system responds adequately to variations in solar input.

Tchaya et al. [63] addressed a related problem in an indirect multitray solar dryer with energy storage and different airflow modes. Using negative temperature coefficient (NTC) sensors to measure temperatures at the inlet, outlet, and on several trays, they designed a proportional temperature controller that actuates fans to maintain the tray temperatures near a target value under fluctuating irradiance [63,68]. Their results show that temperature uniformity across trays depends strongly on the airflow configuration and sensor placement, and that humidity, rather than drying time, is a more robust indicator for determining the end of drying when solar input is unstable. This work illustrates the need to jointly design sensor locations, control laws, and airflow patterns.

4.2. Automatic Monitoring Systems and IoT-Based Solutions

Automatic data acquisition systems have been developed to monitor the most relevant drying parameters (typically product mass, air temperature, and relative humidity) with minimal operator intervention. Paes en [67] implemented an automated monitoring system for a hybrid solar-electric dryer (HSED) that uses load cells, DHT22 sensors, and a low-cost Arduino-based unit to collect and log drying air temperature, relative humidity, and product mass in real time. Communication via Bluetooth and an Android mobile application allows the operator to visualize drying curves and follow the process remotely. The authors validated the system against conventional instruments and noted that the primary limitation was the DHT22 sensor’s reduced accuracy at high relative humidity, particularly for humidity measurements.

Going a step further, IoT-enabled banana dryer implementations report remote monitoring of chamber temperature, relative humidity, and airflow via cloud-based dashboards, with basic on-off actuation for fans/heaters [68,69]. Patil et al. [70] provide an overview of previous developments in solar banana dryers, helping contextualize current IoT approaches. While these IoT implementations are still relatively straightforward, primarily relying on threshold-based control algorithms, they incorporate essential elements for future smart dryers. These include standardized communication protocols, remote access, and integrated data logging. A relevant open challenge is to move from ad hoc dashboards to the systematic use of recorded data for model identification, the optimization of operating policies, and integration with farm- or plant-level decision support systems [36,68,69].

4.3. Smart Sensors and Moisture Estimation

A critical variable in drying is the product’s moisture content, which is challenging to measure continuously without invasive or expensive equipment. Two complementary lines of work have emerged: (i) indirect estimation of moisture from air temperature and relative humidity inside the chamber and (ii) model-based “smart sensors” that use psychrometric relationships and drying kinetics to infer moisture removal.

In a study on Oolong tea drying [71], the authors proposed using combined temperature and humidity sensors to estimate product moisture content during the process. By correlating the measured psychrometric conditions with gravimetric moisture data, they developed regression relationships that allow the moisture content to be inferred from T–RH measurements with acceptable accuracy. This concept is directly transferable to solar dryers for agricultural products, where temperature and humidity sensors are already commonly installed.

Correa-Hernando et al. [72] developed smart sensors for supervising a solar dryer, using multiple low-cost Sensirion sensors distributed within the drying chamber. Their model-based sensors exploit the psychrometric properties of the drying air and the spatial distribution of temperature and humidity to estimate the amount of water removed and to detect non-uniformities in drying conditions within the chamber [72]. These smart sensors provide higher-level information, such as accumulated water removal, local drying rates, and efficiency indicators, which can be used for both online supervision and data-driven modeling. More recently, data-driven models have been used as soft sensors to predict product state during solar-assisted drying. In a solar hybrid collector with dryer (SHCD) for coriander seeds, Saranya et al. [73] compared open sun drying with solar hybrid drying and trained three machine learning models, Gaussian Process Regression, Multilayer Perceptron (MLP), and Radial Basis Function (RBF) networks, to predict “heat-induced dryness” and “mass-based dryness” from measured drying parameters. The tracked variables included air outlet temperature, moisture loss, and solar radiation for 3 kg batches of coriander seeds. Among the models, the RBF network achieved the best performance, with coefficients of determination of about 0.98 for temperature and 0.99 for mass-based dryness, outperforming MLP and GPR. These results show that appropriately selected ML models can act as accurate virtual sensors of dryness for solar hybrid drying systems, potentially reducing the need for direct moisture measurements.

Data-driven smart sensing has also been applied to quality attributes. Rodríguez-Ortiz et al. [74] developed an optimised Multilayer Perceptron neural network to predict visual colour (CIE L*a*b* coordinates) in solar-dried fruits. The model uses nine input variables that capture key solar-drying parameters (e.g., drying air temperature, relative humidity, and solar radiation) and outputs the three CIELAB coordinates for solar-dried oranges. The MLP architecture was tuned using a genetic algorithm, and Bayesian regularization was employed to mitigate overfitting, resulting in excellent predictive performance (R² ≈ 1.0 for training and 0.99 for test data) with low error metrics. The authors emphasize that this model can serve as a practical tool for forecasting color changes during the solar drying of fruits. It can support real-time quality control and process optimization, as well as aid in the offline design and operation of dryers.

Analyzing these contributions makes it possible to find a way toward integrated measurement strategies in which direct measurements—such as air temperature, relative humidity, air velocity, and, when available, product mass—are combined with models to estimate moisture content.

Networked Control Systems and Hybrid Dehydrators

Another line of development focuses on networked control systems (NCS), in which sensors, controllers, and actuators are connected via communication networks, enabling remote supervision and control of hybrid dehydrators. Quiñones-Reyes et al. [75] presented a networked control system for a hybrid dehydrator in which temperature and relative humidity within the drying chamber are measured by distributed sensors, and actuation of heaters and fans is commanded via a supervisory computer connected via an industrial communication protocol [75]. The authors combined experimental design with the NCS architecture to investigate how different operating conditions affect drying performance and to identify suitable control settings. Their work highlights problems as communication delays, the robustness of control under variable network conditions or the importance of a structured experimental methodology for tuning controllers in hybrid solar dryers.

Although very few studies explicitly frame solar dryers within the NCS paradigm, related contributions implement remote monitoring and intelligent control architectures for hybrid solar–electric dryers using communication networks. For example, Hidra in [64] designed and implemented an intelligent remote monitoring and control system for a hybrid solar–electric dryer (HSED) to enhance its performance. Their system integrates sensors and actuators with a remote supervision interface, demonstrating how networked architectures can coordinate the operation of electrical heaters and fans under fluctuating operating conditions [64]. These works show that networked control and remote supervision are technically feasible for hybrid dehydrators. However, they also highlight open challenges in communication reliability, delay compensation, and integrating NCS concepts with IoT-based platforms and advanced control algorithms.

Table 2 summarizes representative monitoring and control techniques reported for solar dryers, ranging from low-cost Arduino-based monitoring with on–off actuation [67] to IoT-enabled remote supervision [64,68,69] and networked control architectures [75]. Related Arduino-based implementations for solar tunnel dryers using low-cost temperature sensors (e.g., LM35) and basic on–off regulation have also reported improvements in temperature control and product quality [76,77].

As summarised in Table 2, most reported implementations still rely on relatively simple rule-based PID controllers, often acting on a single variable such as drying air temperature. These approaches are attractive because of their low cost and ease of implementation, but they offer limited ability to handle strong nonlinearity, varying operating conditions, and product-dependent quality constraints. More advanced strategies, such as fuzzy logic and networked control systems, have been explored in only a few hybrid dehydrators and typically at pilot scale, reflecting a clear trade-off between potential performance gains and design/implementation complexity.

The table also shows that many systems monitor only a subset of the relevant variables, usually chamber temperature and relative humidity, and only occasionally product-mass or air velocity. Low-cost humidity sensors often exhibit accuracy and stability issues at high temperatures and humidity, which directly affect moisture estimation and, consequently, control performance. Recent developments in smart sensors and data-driven estimation techniques demonstrate that higher-level indicators, such as drying rate, accumulated water removal, or quality attributes (e.g., color), can be inferred from basic measurements. However, these tools are still rarely embedded in closed-loop control schemes.

Finally, IoT-based monitoring and control architectures are beginning to appear in solar drying applications, particularly for specific products such as bananas, enabling remote supervision and continuous data logging. However, these control actions remain mostly threshold-based, and few studies report comprehensive efficiency indicators that link sensing and control choices with energy use and product quality. Table 2 shows the necessity for more integrated methods that combine advanced sensing technologies, such as smart data-driven sensors with sophisticated control strategies and standardized performance metrics. This integration is crucial for fully realizing the potential of solar drying systems.

To avoid conflating connectivity with control maturity, Table 2 distinguishes three orthogonal dimensions for each reported system: (i) connectivity and architecture (standalone, IoT-cloud, or networked/SCADA), (ii) control law (on–off, PID, fuzzy, or supervisory), and (iii) sensing and estimation approach (direct sensors, load-cell mass, psychrometric soft sensors, or machine-learning models), together with an explicit indicator of whether the implementation is closed-loop. This separation makes clear that several reported prototypes are monitoring-only systems rather than closed-loop controllers, and that a system can be highly connected yet still rely on simple threshold actuation. The Complexity rating reported in Table 2 follows a simple rubric, scoring hardware parts count and cost, algorithm and tuning effort, networking requirements, and maintenance and calibration burden on a 1–3 scale and mapping the aggregate to Low, Medium, or High. Regarding sensing hardware, the low-cost relative-humidity sensors most commonly reported (e.g., DHT22-class devices) typically offer accuracies of the order of ±2–5% RH and ±0.5–1 °C, with reduced accuracy and slower response near saturation, susceptibility to hysteresis and long-term drift, and sensitivity to condensation—limitations that are particularly relevant at the high humidities seen at dryer outlets. Higher-grade capacitive sensors (e.g., SHT- or BME-class) and industrial probes provide tighter accuracy and better stability but at higher cost, and calibration practice and sensor placement or shielding are rarely reported in the reviewed studies.

To increase understanding of variations in solar dryer performance, Table 3 compares selected studies that used different control and monitoring strategies. It presents experimental outcomes, including final moisture content, energy consumption, and types of sensors used, which are not always apparent from conceptual classifications alone. This synthesis aims to offer practical insights for researchers and designers when assessing the trade-offs between control complexity, data requirements, and field performance.

The studies shown in Table 3 were selected to span the range of control types and dryer classes identified above, prioritizing implementations that report at least one quantitative energy or moisture outcome. Because the reported outcomes mix moisture bases (wet basis, dry basis, or percent extracted) and heterogeneous energy metrics with different system boundaries, Table 3 now distinguishes the moisture endpoint type and basis and labels the boundary of each energy metric (per kg of water removed versus per kg of product; electric-only versus electric plus auxiliary; whether incident solar energy is included), so that apparent cross-study differences are not over-interpreted as physical when they are in fact definitional. To substantiate the standardization argument at the corpus level rather than only through the selected cases, an audit of reporting completeness across all 78 included studies is provided in the Supplementary Material—namely the percentage of studies reporting specific energy consumption or specific moisture extraction rate, the percentage reporting final moisture content with an explicit basis, the percentage reporting air velocity, and the percentage reporting any sensor calibration or measurement uncertainty. Finally, several of the machine-learning and soft-sensing studies report very high coefficients of determination; these results are encouraging but are in most cases internally validated, and their generalization across seasons, loads, and products has not been established. Table 3 therefore reports, where available, the dataset size and the validation type (random split, time split, or external validation) so that the strength of evidence can be interpreted appropriately.

The studies presented in Table 3 differ in scope and technical complexity, but some clear trends can be observed. The following discussion section will examine these trends, highlighting the limitations of current implementations, the challenges in sensing and actuation, and the research opportunities in hybrid, predictive, and data-driven control systems.

5. Discussion and Recommendations

The integration of control and monitoring techniques in solar drying systems has seen notable developments over the last decade, particularly in embedded electronics, sensing, and wireless communication. Traditional controllers, such as proportional-integral-derivative (PID) controllers and basic rule-based systems, continue to be widely used because of their simplicity and minimal hardware requirements, making them suitable for low-resource environments (e.g., [67,70]). As shown in Table 3, their performance is generally acceptable but often lacks the adaptability needed for fluctuating environmental conditions.

From a research-to-application perspective, monitoring and control should be considered an important component of the design process rather than mere add-ons. They play a crucial role in determining whether improvements achieved at the laboratory scale can be successfully applied in the field, especially under varying solar conditions. This approach aligns with the long-standing concern that drying research can become too theoretical and self-referential if operational validation and transfer issues are not directly addressed [39].

A significant limitation in the current literature is the limited use of multivariable or adaptive control strategies. Most systems still rely on single-loop temperature or humidity regulation and rarely incorporate feedback from the actual moisture content of the products. Intelligent control methods, such as fuzzy logic, neural networks, and hybrid strategies, have been proposed (e.g., Saranya et al., Correa-Hernando et al. [72,73]), but these methods remain underutilized in both experimental and commercial settings. Additionally, few studies have validated these systems across different crop types or climatic conditions.

The choice of sensors is a significant limitation in many systems. Low-cost solutions often rely on basic temperature and relative humidity sensors, such as the DHT22, which suffer from low accuracy and drift over time. Only a limited number of studies, such as those by Hidra et al. [64], showcase integrated multi-sensor systems that offer real-time feedback and adaptive actuation. Furthermore, the lack of standardized sensor calibration and redundancy hinders the development of robust control in most of the systems reviewed.

Energy efficiency and final moisture content also vary significantly across designs. For example, the dual-stage airflow system of Ekka et al. [60] demonstrated low specific energy consumption (0.56 kWh per kg of water removed in its two-stage case, compared with 1.07 kWh per kg of water removed in the single-stage case) while maintaining acceptable drying quality, whereas more advanced systems often incur increased computational or energy overhead due to continuous sensing and control tasks. Despite these differences, very few studies include a complete techno-economic assessment or address long-term energy savings.

From a design standpoint, there is an emerging trend toward modular, remotely monitored systems that often incorporate IoT interfaces. However, many remain tailored to specific crops or conditions and lack the generalizability needed for broader adoption. The interoperability of control systems with renewable energy inputs or grid-tied infrastructure also remains an underexplored opportunity.

The literature reflects some increasing sophistication in proposed solutions alongside fragmentation. Future work should prioritize integrated control architectures, sensor reliability, and performance validation under realistic operating conditions, with a stronger emphasis on economic feasibility, field durability, and multi-crop adaptability.

6. Conclusions

This review has examined a broad range of control and monitoring techniques used in solar drying systems, categorizing them into three main types: conventional approaches (e.g., PID, proportional logic), logical (rule-based) methods, and advanced techniques (e.g., fuzzy logic, neural networks, IoT integration). The inclusion of performance comparisons (see Table 3) has provided a clearer understanding of how different strategies influence energy consumption, product quality, and system complexity.

In contrast, intelligent approaches, such as the RBF-based model discussed in Saranya et al. and the smart-sensing/model-based estimation framework from Correa-Hernando et al. [72] can achieve superior predictive performance. However, their application beyond laboratory or pilot scales remains limited due to factors such as hardware and integration costs, data scarcity, and the lack of standardization and interoperability [8].

An important finding is the lack of integrated frameworks that scale sensing, actuation and adaptive control. Moreover, the absence of standardized testing protocols and techno-economic analyses impedes comparison and broader adoption.

To advance future innovation, we recommend the following directions: develop modular hybrid control strategies that leverage PID robustness and AI adaptability; standardize sensor selection, placement, and calibration to enhance data reliability and system stability; implement predictive models with real-time feedback, ideally trained on field-collected data under various drying conditions; conduct comprehensive techno-economic analyses for both commercial and decentralized implementations; and promote interoperability with off-grid renewable energy sources to improve sustainability and deployment flexibility.

These directions will require technical improvements and a system-level design mindset that accounts for usability, cost, and scalability across diverse agricultural contexts.