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Article

Computer Model of an IoT Decision-Making Network for Detecting the Probability of Crop Diseases

by
Grygorii Diachenko
1,
Ivan Laktionov
2,*,
Oleksandr Vovna
3,
Oleksii Aleksieiev
4 and
Dmytro Moroz
2
1
Department of Electric Drive, Faculty of Electrical Engineering, Dnipro University of Technology, Dmytra Yavornytskoho Ave., 19, UA49005 Dnipro, Ukraine
2
Department of Computer Systems Software, Faculty of Information Technologies, Dnipro University of Technology, Dmytra Yavornytskoho Ave., 19, UA49005 Dnipro, Ukraine
3
Department of Software Systems and Technologies, Faculty of Information Technology, Taras Shevchenko National University of Kyiv, Volodymyrska Str., 60, UA01033 Kyiv, Ukraine
4
Department of System Analysis and Control, Faculty of Information Technologies, Dnipro University of Technology, Dmytra Yavornytskoho Ave., 19, UA49005 Dnipro, Ukraine
*
Author to whom correspondence should be addressed.
IoT 2025, 6(1), 8; https://doi.org/10.3390/iot6010008
Submission received: 15 December 2024 / Revised: 17 January 2025 / Accepted: 20 January 2025 / Published: 21 January 2025
Browse Figures
Review Reports Versions Notes

Abstract

:
This article is devoted to the development and testing of a computer model of an IoT system that combines wireless network technologies for the online monitoring of climatic and soil conditions in agriculture. The system supports decision-making by predicting the probability of crop diseases. This study focuses on the processes of aggregation, wireless transmission, and processing of soil and climatic measurement data within infocommunication software and hardware solutions. This research makes both scientific and practical contributions. Specifically, it presents a computer model based on wireless sensor networks and edge-computing technologies. This model aggregates and intelligently processes agricultural monitoring data to predict crop diseases. The software component, developed using an adaptive neuro-fuzzy inference system (ANFIS), was integrated into the microcontroller unit of IoT systems for agricultural applications. This approach enabled the substantiation of an optimised algorithmic and structural organisation of the IoT system, enabling its use in designing reliable architectures for agricultural monitoring systems in open fields with decision-making support.

1. Introduction

1.1. Importance of the Topic and Motivation for Research

Agriculture provides livelihoods for a large portion of the world’s population and plays a pivotal role in global and regional economies. However, in open-field agriculture, where crops are exposed to various environmental factors and potential risks, ensuring crop preservation becomes a critical task. Unpredictable weather conditions, pests and diseases pose serious challenges that can significantly affect agricultural crop yields and quality [1,2]. For instance, the Food and Agriculture Organization (FAO) estimates that plant diseases account for 20–40% of global crop production losses annually [1]. These losses not only impact farmers but also disrupt food supply chains and contribute to economic instability.
Given the multifaceted importance of agriculture, it is becoming imperative to incorporate cutting-edge technologies to protect crops and increase productivity [3,4]. The integration of the Internet of Things into agriculture has proven to be transformative, offering monitoring in real-time mode, data-driven decision-support, and automation [5]. In particular, in the context of crop security, IoT technologies contribute to the timely detection and mitigation of threats, minimising economic losses and ensuring a stable food supply.
The development of advanced technologies is a practical necessity for solving the complex problems of modern agriculture. In the effort to develop effective IoT solutions for agriculture, the role of modelling methods cannot be overestimated. Modelling is a fundamental step in the end-to-end development cycle of IoT systems [6]. Modelling allows for the simulation and analysis of proposed solutions before deployment, providing insight into system behaviour, potential problems, and areas for improvement. In the case of crop disease prediction, modelling is becoming a critical step in enhancing algorithms and optimising the performance of wireless sensor networks [7].
The key aspects that require further development and study are as follows: the development of software and hardware solutions for network data exchange by combining wireless technologies according to the distance of data transmission; consideration of the species and periods of crop vegetation when implementing algorithms and computerised means of monitoring systems; accounting for the interconnectedness of informative and destabilising chemical, physical, and biological parameters that impact cultivation efficiency and the probability of the development of diseases of agricultural crops; and the creation, verification, and optimisation of computer models for the aggregation and comprehensive processing of agro-monitoring data based on relevant machine learning and artificial intelligence techniques. These models should be convertible into software for the edge level of monitoring and automation tools and systems for agricultural enterprises.
This article is dedicated to the development and testing of a computer model of an IoT system based on a combination of wireless network technologies and aimed at implementing online monitoring of the climatic and soil conditions of agricultural enterprises with decision-making support for the management of agrotechnical processes by predicting the probability of occurrence of crop diseases. The object of this research study is the processes of the aggregation, wireless transmission, and processing of climatic and soil data in infocommunication hardware and software solutions. The subject of this research is the computerised model of an IoT system on the basis of wireless sensor networks (WSNs) and edge-computing technologies.
Therefore, the relevance of the research topic is emphasised by the pressing need for sustainable and efficient agriculture. As the world’s population constantly grows, the demand for food is increasing, making it necessary to optimise agricultural processes. The research results will contribute to creating a reliable computing architecture for monitoring systems in open fields with decision-making support.

1.2. Review, Comprehensive Analysis, and Logical Systematisation of Relevant Literature Sources

Over the past two decades, the global agricultural production of major agricultural crops has shown a steady upward trend to meet growing international demand (see Figure 1a): the growth rate of 56% recorded between 2000 and 2022 was driven by improved production technologies and intensification of agricultural activities, including the broader use of irrigation, pesticides, fertilisers, and high-yielding crop varieties, as well as expansion of cultivated areas amid the negative effects of climate change [8,9]. Corn, wheat, rice, barley, and sorghum were the five most widely grown cereals in 2022 (see Figure 1b) [8,9].
In 2022, crop production growth was +0.7%, which was caused by a general recession, probably due to market effects after the outbreak of the war in Ukraine, as well as high inflation [10]. According to the new forecasts published in the latest Cereal Supply and Demand Brief [11], which was released on 02 February 2024, global cereal production in 2023 will reach a record high of 2836 million tonnes, which is 1.2% more than in 2022 [10].
Thus, considering the continuous dynamics of the areas for crop cultivation, as well as the volumes of cultivation [8,9], the issues related to the development, modernisation, and enhancement of distributed climate and soil online-monitoring systems that are integrated into IoT networks for agrotechnical purposes are of great relevance [12].
The resilience and sustainability of the agricultural sector depend on integrating information technology (IT). Over the past decades, IT has become an important factor that has revolutionised traditional farming methods. From precision farming to data-driven decision-making, the transformative impact of IT is apparent in optimising resource utilisation, increasing yields, and improving overall farm management. In the broader context of IT, IoT is becoming a catalyst for unprecedented progress in agriculture. IoT technologies facilitate the seamless connection of devices, sensors, and systems in real time, contributing to creating a networked agricultural ecosystem. This connectivity provides farmers with invaluable information that allows them to monitor, analyse, and respond to dynamic environmental conditions and crop-specific needs. According to Juniper, the number of IoT connections will grow to 83 billion by 2024 from 35 billion in 2020, with about half of them falling into the MIoT category [13]. At the same time, IndustryARC predicts a 7.1% CAGR for the mass IoT market, which will reach USD 121.4 billion by 2026 [14].
In this regard, Industry 4.0 (I4.0) and Agriculture 4.0 (AC4.0), which combine traditional agriculture with various modern cutting-edge technologies, can help improve food supply and food security. This covers all digitalisation, computerisation, and automation processes in business and our daily lives, including big data, artificial intelligence (AI), IoT, virtual and augmented reality, technologies such as unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), image processing, machine learning (ML), fog and edge computing, cloud computing, and WSNs, which are expected to bring significant changes in the industry. Currently, the direction of advancement in highly efficient applied information technologies necessitates architectural solutions that fulfil complexity and systematicity criteria, aligning with the conceptual frameworks of I4.0 and AC4.0. This involves executing a complete sequence of functional transformations, encompassing the precise aggregation of field data, dependable network exchange, and analytical computing in fog, edge, or cloud environments [15,16,17,18,19,20,21,22,23,24,25,26].
At the network level, the intricacies of protocol interactions and sensor placement play a key role in the efficiency of agricultural IoT systems. High-performance communication protocols ensure reliable data exchange, while various sensors collect vital information about soil conditions, weather conditions, and crop health [27,28,29,30,31,32,33,34,35]. This networked approach forms the foundation of intelligent decision support systems for farmers, offering a comprehensive accounting of their agricultural operations [36,37,38,39,40,41,42,43].
At the development stage, computer modelling becomes a highly important tool for improving and optimising agricultural IoT systems. Modelling makes it possible to simulate different scenarios, refine algorithms, and evaluate system performance under different conditions. This iterative process ensures the reliability and efficiency of the IoT infrastructure, laying the foundation for a sustainable and adaptive agricultural monitoring system [44,45,46,47,48,49,50,51,52].
Embedded software, as an integral component of IoT systems, is central to predicting and extracting valuable information from spatially and temporally distributed agricultural monitoring data. Using sophisticated AI and ML algorithms and techniques, embedded software transforms raw data into meaningful predictions. This intelligent data analytics is crucial for disease prediction, resource optimisation, and intelligent decision-making, which contributes to the main aim of increasing agricultural yields and productivity [53,54,55,56,57,58,59,60,61,62,63,64,65].
The path from the broader realm of information technology to the intricacies of IoT, network protocols and sensors, culminating in the importance of computer modelling and embedded software, highlights the multifaceted role that technology plays in shaping the modern agricultural landscape, as shown in Table 1. It is important to acknowledge that the findings presented in Table 1 from the analysis of scientific sources are not comprehensive. Rather, they highlight predominant sustainable development trends and challenges in the field of IoT systems architecture development, data transmission protocols interaction, computer modelling, and intellectualisation of information and computer technologies for agro-technical monitoring and management, and they prove the effectiveness of ML and AI integration into pertinent technical systems and networks.
Based on the systematisation of the results of the literature analysis, the following has been established: when building IoT systems, a three-tier architecture is used (perception level, transport level, and software level); WSN technology is used to create the physical infrastructure of IoT systems; ZigBee, LoRa, Bluetooth, Wi-Fi, and LTE technologies are utilised for network data exchange; IoT Cloud is utilised for the aggregation and intelligent processing of measurement data; and the synthesis of the algorithmic and structural design of WSNs, in most cases, is performed based on a single-factor approach according to the criteria of network coverage area or energy efficiency.
Therefore, the conducted analysis and generalisation of the known results of scientific and practical research in the area of IoT monitoring of distributed objects in real-time mode, with the characteristic features of agricultural crop production as monitoring objects, allowed for the localisation of the range of tasks that require further research in this article: the clarification of the designed systems’ architecture at the early stages of development using computer modelling methods; development and validation of a computer model of various protocols’ network interaction at different architectural levels of the IoT system; development of algorithms for intelligent data processing at the low level (microcontrollers) to transform measurement data, which will enable the transition to edge computing technology without fundamentally changing the architecture of the IoT networks currently used for agrotechnical monitoring.
Thus, conducting research in the areas mentioned earlier will improve the known systems of real-time IoT monitoring of climatic and soil parameters of crop production enterprises through the substantiation and implementation of a computer model and means of complex aggregation, network exchange, and intelligent data processing aligned with the scientific and practical principles of edge computing technology—as well as of monitoring the influence of climatic and soil factors on the efficiency of growing crops in real time with decision-making support based on the concepts of IoT, ANFIS and XAI—which will increase the rational use of the resources and areas involved during the full cycle of crop production.

1.3. Novelty and Key Contributions of This Study

The main scientific and practical value of the results of this article is the modernisation of the hardware and software of IoT technologies for the soil and climate monitoring of open-field agricultural enterprises. The proposed computer-oriented model of the IoT network is based on edge-computing technology and WSN and, unlike previously known ones, implements an intelligent algorithm for analysing the integral impact of a collection of climatic parameters (air temperature and humidity, precipitation, and time duration of leaf cover in a moistened state) on the probability of the occurrence and development of specific types of diseases of specific crops, based on the network integration of precise and prompt measurement monitoring, reliable data and information exchange, and intelligent data processing with real-time decision support embedded in the microcontroller link. Intelligent data processing is carried out at the edge of the network (locally), which allows for the implementation of edge-computing techniques based on low-cost sensor and microprocessor devices with embedded software based on XAI algorithms.

1.4. Structure and Organisation of This Paper

The structure of this article is outlined as follows: in the first section, the significance of the research topic is substantiated, information analysis and systematisation of current scientific findings within the subject area under investigation are carried out, and the main aim, object, subject, and anticipated outcomes of the research are determined; in the second section, the approaches, methods, and means of the research are described in detail; in the third section, the results of research on the structural and algorithmic organisation of IoT via computer experiment are presented; in the fourth section, the perspective directions for further research are substantiated; and in the fifth section, the general conclusions are presented.

2. Materials and Methods

2.1. Overall Characteristics of the Research Methods and Means

The research findings of this article were obtained utilising the following methods: comprehensive analysis and generalisation of the well-known results of scientific research in the area of IoT monitoring; theory of adaptive systems of neuro-fuzzy computing; theory of identification of nonlinear dynamic systems; computer simulation of information and communication networks and systems; and testing and validating of the structural and algorithmic organisations of IoT monitoring networks. This research presented in this article represents a natural progression of the authors’ prior theoretical and experimental investigations concerning the advancement of smart IoT systems for monitoring agrotechnical entities, as documented in scientific articles [12,41,50,58]. The validity of the acquired results was confirmed by testing a computer model depicting the synthesised algorithmic and structural framework of the IoT agrotechnical monitoring system within the Proteus simulation environment.

2.2. Generalised Structural Description of Computer-Oriented Model

The basis of the developed computer model of the IoT network for detecting the probability of crop diseases with decision-making support is the WSN technology based on the topology of the star of stars (see Figure 2). This diagram illustrates the data flow from sensor nodes to the cloud server, highlighting the integration of wireless communication technologies and edge computing. In this structure, each functional unit “Local field WSN network #” represents a location (a separate fragment “Type B” in Figure 3) that collects data from end nodes (a set of smart soil and climate sensors) using NRF24 at an ISM operating frequency of 2.5 GHz (Type A in Figure 3). Afterwards, the data collected from Type A network nodes that have undergone preliminary statistical analysis procedures in the matching Type B network nodes (time and space averaging) are transmitted via LoRa technology to the field network infrastructure’s base station (Type C). The core functions of the base station (Type C network node) are as follows: creation of a local data base of measurement results; intelligent data processing and analysis with the possibility of predicting the impact of climatic and soil parameters on the quality of crop cultivation; coordination of communication network protocols; sending information (processed data) to the remote (cloud) server using LTE technology. The cloud server in this hierarchical architecture (see Figure 2) functions as an IoT platform, enabling access to the visualisation of measurement data and information on remote user devices.
The above-mentioned wireless communication technologies and the functional and structural organisation of the information and communication infrastructure of the IoT network, shown in Figure 3, were selected based on research conducted in [58] substantiating the optimal geometric model for the placement of sensor nodes according to the criteria of reliability and coverage area, taking into account the range, power consumption, and data encryption algorithms.
Figure 4 and Figure 5 refer to the intellectual component of the designed computer model of the IoT system.
The dataset is the basis for training the ANFIS model [58]. It contains historical data such as date and time of measurement, air temperature, relative humidity, precipitation, and leaf wetness time. Each record in the dataset represents a specific observation in the context of agriculture, and the target variable is the presence or absence of the probability of crop disease.
It is important to ensure data quality by properly handling missing values. In this step, records containing zero values are filtered out. Missing data can have a negative impact on the training process and model accuracy, so it is important to either impute or delete such records. The time feature of the data may not be directly relevant to disease prediction, and keeping it in the dataset may result in unnecessary complexity. Therefore, this parameter is removed.
Splitting a dataset into training and validation sets is a common practice in machine learning. The training set is used to train the ANFIS model on patterns in the data. In contrast, the validation set helps to evaluate the model’s generalisation performance on data outside the training set. This step is crucial to prevent overfitting.
Training the ANFIS model involves optimising its parameters according to the patterns in the training data. During this phase, the model learns the relationships between the input features and the target variable (the probability of a crop disease). The learning process is aimed at minimising the error between predicted and actual results.
After training the ANFIS model, its performance is evaluated on a validation sample. This step indicates how well the model generalises to new data according to standard metrics such as MAE, RMSE, and R2.
If the model shows signs of overfitting (performing well on the training set but poorly on the validation set), adjustments should be made to prevent this. Overfitting occurs when the model learns noise in the training data rather than the underlying patterns. This step involves adjusting hyperparameters.
If the ANFIS model does not show overfitting and performs well on the validation set, it can be used to predict the probability of disease occurrence.
The detailed UML diagram describing each stage of the proposed model for predicting the probability of a specific disease in a specific crop is shown in Figure 5. Together, Figure 4 and Figure 5 show the complete process of the proposed methodology, spanning from data collection to precise prediction utilising ANFIS.

2.3. Model Limitations

The following conditions and limitations were taken into account during the research:
  • The dataset (spanning from September 2022 to September 2023) containing climatic parameters was gathered from the Metos by Pessl Instruments weather station, utilising the FieldClimate IoT platform. Access to this platform was granted by Metos Ukraine LLC;
  • The agroclimatic zone for data collection was the northern steppe of Ukraine, characterised as arid and warm (with a hydrothermal coefficient ranging from 0.7 to 1.0). The typical annual temperature sum ranges from 2900 °C to 3300 °C
  • The agricultural crop under study was corn;
  • The diagnosed disease of interest was Fusarium Head Blight;
  • Informative climatic and soil parameters included air temperature (°C), relative humidity (%), precipitation (mm), and leaf wetness time (min).
The field-level unit for collecting measured data in the investigated model was implemented using standard components of the Proteus modelling environment. Arduino Uno and Arduino Mega 2560 were used as microcontrollers. The components used to build the model are shown in Table 2. Network communication was provided by means of HC-05 based on Serial Interface, Modulo RF library, and Sim900D. The visualisation device was a virtual terminal.

3. Results

3.1. Development and Modelling Outcomes of the Functional Components of the IoT System

Based on the scheme of the functional and structural organisation of the information and communication infrastructure of the IoT agrotechnical monitoring network (see Figure 3), while considering the approach of decomposing the research task involved in developing and evaluating computer models of the functional and structural components of information technology, a simulation of the computer model was implemented in the Proteus software environment to test the network interaction of various protocols at different architectural levels of the IoT system, as well as to test the integrated intelligent algorithm for predicting the probability of occurrence of crop diseases. The corresponding ANFIS model for data processing depicted in Figure 4 was developed using the Matlab & Simulink Fuzzy Logic Toolbox [58]. These computer models reflect the approach previously described in Section 2.2, “Generalised structural description of computer-oriented model”. The data utilised for training and evaluating the generated models were derived from the authors’ original experimental investigations, detailed in articles [12,58]. The procedure for constructing and assessing the computer model is outlined as follows:
  • The ANFIS model acquired in [58] was converted into software code tailored for the Arduino Mega microcontroller platform utilising a specialised open-source online tool (refer to Appendix A). Subsequently, adjustments were made to the arguments of the software components’ functions to ensure alignment with the involved microcontroller pin numbers and the ranges of variation in physicochemical soil and climatic parameters.
  • A computer simulation model of a Type A network node utilising Arduino Uno Rev3 within the Proteus environment, as mentioned in Appendix A, was developed and is depicted in Figure 6. This simulation model integrates software developed in the Arduino IDE environment (see Appendix A), which implements the acquisition of soil and climate data from sensors, preliminary statistical analysis (time and space averaging), and the transmission of measurement data to the Type B network node using the NRF module.
  • A computer simulation model of the Type B network node utilising Arduino Mega 2560 in the Proteus environment, as detailed in Appendix A and illustrated in Figure 7, was developed. This simulation model integrates software developed in the Arduino IDE (see Appendix A), which aggregates measurement data from Type A network nodes, polls its own soil and climate sensors, performs preliminary statistical analysis (time and space averaging), and transmits the result to the Type C base station using LoRa technology (see Figure 8).
  • This simulation model includes software code that aggregates measurement data from Type B network nodes, polls its own soil and climate sensors, performs preliminary statistical analysis (time and space averaging), uses ANFIS to predict the probability of the occurrence of the crop disease, and sends an SMS with the result of the intelligent analysis to a specified number.
  • The modes of functioning of the created computer model were tested and validated by detecting data transmitted as a result of network interaction of various protocols at different architectural levels of the IoT system using a virtual terminal. These steps enabled an evaluation of the accuracy and resilience of the proposed hardware and software solution.
The test models that have been developed serve as a simulation foundation for conducting further research on the developed information technology through computer experiments.

3.2. Outcomes of the Development and Modelling of the Network Architecture of the IoT System

Based on the above provisions of developing the computer model of the IoT network for detecting the probability of occurrence of crop diseases with decision-making support, its investigation utilised computer modelling methods, along with a thorough analysis of the results obtained, encompassing both qualitative and quantitative aspects.
The appearance of the entire model is shown in Figure 9. Structurally, the model is built in accordance with Figure 3 of Section 2.2, “Generalised structural description of computer-oriented model”. Due to the computational limitations of the Proteus environment, an MVP version of the diagram of the structural and functional organisation of the information and communication infrastructure of the IoT agrotechnical monitoring network is implemented in Figure 3: 2× Type A nodes, 1× Type B and 1× Type C (see Figure 9). The operation of individual network nodes and the description of how the model implements the communication between Type A, Type B, and Type C are described in Section 3.1, “Results of development and modelling of functional components of the IoT system”.
The corresponding results of validating the network interaction of different protocols at different architectural levels of the IoT system are presented in the form of screenshots of virtual terminals of the Proteus environment (see Figure 10, Figure 11, Figure 12, Figure 13 and Figure 14). The corresponding results of testing the integrated intelligent component of detecting the probability of crop diseases are presented in the form of 2D graphs of the dependence of climatic and soil parameters of the growing area on time and the probability of disease occurrence under different combinations of these factors (see Figure 15). In Figure 15, the period from 10 August 2023 19:00 to 11 August 2023 11:00 is presented, where the first seven points with an interval of one hour are prehistory, and from 11 August 2023 02:00, the prediction begins. The main objective of this experiment was to test data transfer within the network interaction of different protocols at different architectural levels of the IoT system and test the edge computing technology of the ANFIS model in the microcontroller unit to predict the probability of occurrence of crop diseases under the complex influence of informative factors.
Proteus test results demonstrated the validity and reliability of the developed computer model. The emulation of network nodes, base station, and communication protocols accurately reflected the real-world scenario, providing up-to-date information about the behaviour of the system. In addition, the successful data transfer between the modules confirmed the effectiveness of the implemented communication mechanisms, such as NRF and LoRa technologies.
The integration of ANFIS for disease probability prediction in combination with SMS messages via LTE demonstrated the capacity of the system to perform advanced analytics and provide end users with information on the detected probability of a particular disease for a specific type of agricultural crop.
Based on the test results (see Figure 15) using the formulas for calculating the mathematical expectation (ΔProbabilitymean) and standard deviation (σΔProbability), the probabilistic characteristics of the error of the approximation model were estimated:
Δ Probability mean = 1 n ∑ i = 1 n Probability Dataset i − Probability Model i ;
σ Δ Probability = 1 n − 1 ∑ i = 1 n Δ Probability i − Δ Probability mean 2 ,
where
ProbabilityDataset is the probability of disease occurrence from the FieldClimate IoT platform dataset;
ProbabilityModel is the predicted probability of disease occurrence, the output of the ANFIS model.
The testing phase included evaluating the performance of the IoT system under various environmental conditions. Key findings include the following:
  • Based on the performed calculations, it was determined that the mathematical expectation of the data approximation is 5.2% with a standard deviation of ±1.4%. It was also found that this error is additive and can be reduced by introducing a correction to the approximation results. Numerically, this correction is equal to the mathematical expectation of the error with the reverse sign (see Figure 15, green dots—predicted with correction). After introducing the correction, the error value does not exceed (1.1 ± 0.7)%.
  • The system effectively aggregated data from multiple sensors and transmitted it reliably across architectural levels using LoRa and NRF24 protocols.
  • Real-time edge computing capabilities ensured timely predictions and decision-making support for mitigating disease risks.
The outcomes of the computer experiment, depicted in Figure 10, Figure 11, Figure 12, Figure 13, Figure 14 and Figure 15, affirm the suitability of the developed system for utilisation in software and hardware tools for intelligent monitoring of climatic and soil conditions, as well as for detecting potential crop diseases. Consequently, this enables the deployment of intelligent edge computing technology based on cost-effective embedded microprocessor and sensor technologies within open-field agrotechnical production environments. Moreover, the proposed hardware and software solutions for advancing corresponding information and communication technology can be customised for a broader array of crops and expanded to encompass a greater spectrum of influential factors and physical and chemical parameters, all without necessitating fundamental alterations to the architecture of the designated technology. This study underscores the positive impact of enhancing the functional and technical attributes of presently utilised agrotechnical information and computer technologies for monitoring and management.

4. Discussion

The primary scientific and applied impact in this article is creating the computer model of the IoT network based on WSNs and edge computing technologies using intelligent information technology for the agricultural monitoring and detection of the probability of crop diseases. It provides decision-making support that involves synthesising the algorithmic and structural organisation of pertinent hardware and software solutions and leveraging the conceptual frameworks of IoT and neuro-fuzzy logic. By integrating IoT technologies with ANFIS-based data processing, farmers can monitor and mitigate crop diseases more effectively. For instance, the system could be deployed in cornfields to detect Fusarium Head Blight early, allowing farmers to take preventive measures such as adjusting irrigation schedules or applying fungicides selectively. This targeted approach reduces costs and environmental impact compared to blanket treatments.
The practical utility of the system extends beyond disease prediction. Its real-time decision-making capabilities can help optimise other agricultural practices, such as nutrient management and pest control. For example, integrating the system with weather prediction models could enable dynamic adjustments to farming schedules, further enhancing productivity.
There are several challenges that may be relevant in the practical implementation of the proposed model and require further elaboration:
  • Accounting for aggressive environmental conditions requires more thorough research on the reliability of microelectronic components.
  • The impact of the battery life of portable power modules on the continuous operation of an IoT system needs to be investigated.
  • The influence of natural and artificial interference on signal transmission efficiency over certain distances in actual conditions needs to be assessed.
Despite these challenges, this study demonstrates that IoT systems with embedded edge computing can play a transformative role in modern agriculture.
The key focus areas for future research of the developed computer-based model include conducting long-term testing accompanied by iterative refinement of the pertinent hardware and software solutions and engaging in expert analysis of time-series data derived from experimental observations across a broader spectrum of crops, considering more informative parameters and destabilising factors. Subsequent research directions include integrating the identified patterns into software of information technologies and undertaking a comprehensive technical and economic evaluation of the proposed software and hardware solutions.

5. Conclusions

This article solves an important scientific and applied problem regarding the development and testing of the computer model of an IoT system based on a combination of wireless network technologies aimed at implementing the online monitoring of the soil and climatic conditions of agricultural production with support for decision-making on the management of agrotechnical processes by predicting the probability of the occurrence of crop diseases.
The primary scientific and practical divergence between the research outcomes delineated in this article and those previously documented lies in the comprehensive consideration of soil and climatic parameters’ intricate influence across temporal and spatial dimensions throughout the entire growth cycle of various crop varieties. This consideration is embedded within the software and hardware framework of information technology for intelligent computerised monitoring and prediction of crop disease probabilities. This technology amalgamates networked wireless technologies of varying ranges, a cost-effective component base, and edge computing methodologies.
Such an approach facilitates intricate embedded smart processing of measurement data at the microcontroller level within industrial automation systems tailored for agricultural applications within the context of Industry 4.0 and Agriculture 4.0 paradigms. The resultant efficacy stems from a synergistic amalgamation of cutting-edge advancements in IoT, WSNs, and ANFIS, leveraging the latter as a methodology for eXplainable Artificial Intelligence (XAI).
The main results of this article are as follows:
  • A comprehensive analysis of the subject area of digitalisation of agriculture was carried out and allowed us to localise the directions of perspective research of this article, taking into account modern scientific and applied achievements in the field of IoT systems, approaches to their computer modelling, and intelligent analysis technology for time series of measurement monitoring results.
  • The structural and algorithmic organisation of the information and communication infrastructure of the agrotechnical monitoring network of the IoT system was developed. It implements the principles of edge computing and takes into account the results of previous studies. It also reflects on their implementation, considering the integral influence of the criteria that determine the number of wireless network nodes and the reliability of measurement data exchange.
  • The computer model was implemented in the Proteus environment, which allowed us to test and validate the network interaction of various protocols at different architectural levels of the IoT system according to the criterion of the objective testing of algorithms for multi-level data aggregation, processing, and transmission.
  • Data processing software based on ANFIS technology was developed for the microprocessor unit of the system. This allowed for the analysis of the results achieved at both qualitative and quantitative levels.
  • The data approximation error was estimated at (5.2 ± 1.4)%. As a result, an approach to its reduction was proposed based on introducing a correction to the prediction results. The value of the error after compensation does not exceed (1.1 ± 0.7)%.
This research contributes to sustainable agriculture by promoting the efficient use of resources, reducing chemical usage through targeted interventions, and minimising yield losses caused by crop diseases. The overall scientific and practical impact of the research presented in the article facilitates improving the functionality of scaling the monitoring zone and adapting it to different types of diseases and types of crops in open fields by introducing hardware and software tools for the early detection and prediction of the dynamics of crop diseases based on the network integration of precise and prompt measurement monitoring, reliable data and information exchange, and intelligent data processing with real-time decision support embedded in the microcontroller link. The research findings serve as the hardware and software foundation for advancing the digitisation and intellectualisation of industrial ecosystems within the agricultural sector. This advancement is achieved by using embedded computer-oriented tools employing edge architecture.
A set of promising areas of research to improve the adequacy, adaptability, and scalability of the developed computer-oriented model using XAI for diagnosing crop diseases is also substantiated in this article.

Author Contributions

Conceptualization, I.L.; methodology, I.L.; software, G.D.; validation, I.L.; formal analysis, D.M.; investigation, G.D. and O.V.; data curation, D.M.; writing—original draft, G.D.; writing—review & editing, O.V. and O.A.; visualization, O.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was carried out as part of the scientific project ‘Development of software and hardware of intelligent technologies for sustainable crop production in wartime and post-war’ (0124U000289) funded by the Ministry of Education and Science of Ukraine at the expense of the state budget.

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. The Software Used

References

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Iot 06 00008 g001
Figure 1. World agricultural crop production volumes and growth and production of top cereals [9]: (a) world agricultural crop production volumes and growth [9]; (b) world production of top cereals [9].
Figure 1. World agricultural crop production volumes and growth and production of top cereals [9]: (a) world agricultural crop production volumes and growth [9]; (b) world production of top cereals [9].
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Figure 2. Generalised architecture of the information and communication infrastructure of the IoT network for agrotechnical monitoring [12].
Figure 2. Generalised architecture of the information and communication infrastructure of the IoT network for agrotechnical monitoring [12].
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Figure 3. Detailed functional and structural organisation of the information and communication infrastructure of the IoT agrotechnical monitoring network.
Figure 3. Detailed functional and structural organisation of the information and communication infrastructure of the IoT agrotechnical monitoring network.
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Figure 4. Proposed ANFIS model for disease prediction.
Figure 4. Proposed ANFIS model for disease prediction.
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Figure 5. UML diagram of the proposed model.
Figure 5. UML diagram of the proposed model.
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Figure 6. Node Type A.
Figure 6. Node Type A.
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Figure 7. Node Type B.
Figure 7. Node Type B.
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Figure 8. Node Type C.
Figure 8. Node Type C.
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Figure 9. View of the full model.
Figure 9. View of the full model.
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Figure 10. Virtual terminal Node Type A #1.
Figure 10. Virtual terminal Node Type A #1.
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Figure 11. Virtual terminal Node Type A #2.
Figure 11. Virtual terminal Node Type A #2.
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Figure 12. Virtual terminal Node Type B.
Figure 12. Virtual terminal Node Type B.
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Figure 13. Virtual terminal Node Type C.
Figure 13. Virtual terminal Node Type C.
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Figure 14. Virtual terminal SIM900D.
Figure 14. Virtual terminal SIM900D.
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Figure 15. Graphical representation of the ANFIS model’s predictions against observed data.
Figure 15. Graphical representation of the ANFIS model’s predictions against observed data.
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Table 1. Results of analysis and systematisation of scientific and applied results regarding the development and modelling of IoT systems for distributed monitoring in real-time mode.
Table 1. Results of analysis and systematisation of scientific and applied results regarding the development and modelling of IoT systems for distributed monitoring in real-time mode.
Research SubjectTechnologies UsedScientific Source
Development of scientifically grounded approaches to improving the performance of IoT systems for agrotechnical monitoring based on the optimality criterion, which considers the simultaneous influence of factors such as maximum uptime of hardware and software tools, maximum coverage area of the network, and minimum quantity of sensor wireless nodes used.WSN, ZigBee, LoRa, LTE, IoT cloud,
CupCarbon		[12]
Development of scientific and applied approaches to improve computer-integrated microclimate monitoring systems for industrial agricultural greenhouses.GSM/GPRS,
IoT Cloud		[50]
Development of a farm management system based on embedded systems, IoT, and WSNs for agricultural field and livestock farms.IoT, WSN, GSM,
Wi-Fi		[66]
A framework that combines the sensor, network, and visualisation layers to observe progressive trends in environmental data while being cost-effective.IoT, EnviroDIY,
Python		[67]
The development of an information system for assessing air quality based on data from ground stations and monitoring meteorological data will solve the problem of sending out alerts about danger to people.Docker, REST, API, CALPUFF, WRF[68]
An alert system for monitoring water deficit in plants using IoT technologies.IoT cloud,
WSN, ZigBee		[69]
WSN using ZigBee and LoRa communication protocols for integration into energy management systems of smart buildings.WSN, ZigBee,
LoRa		[70]
Development of a microcontroller system for monitoring the radiation background using the Arduino Uno board and the Geiger counter SBM-20.Petri nets,
Geiger–Mueller
counter		[71]
Building an energy-efficient, resilient WSN while maximising node density and coverage using the FCM clustering algorithm.WSN, FCM[72]
Investigation of the performance of a heterogeneous WSN system using hybrid LoRa-Zigbee communication.ZigBee, LoRa,
MQTT,
ThinkSpeak, Blynk		[73]
The research focuses on optimising energy efficiency in wireless-powered mobile edge computing (WP-MEC) networks with multiple hybrid access points (HAPs) by proposing a Two-stage Multi-Agent deep reinforcement learning-based Distributed computation Offloading (TMADO) framework that jointly optimises energy, computation, and resource allocation in dynamic environments.Multi-HAP WP-MEC network, TMADO[74]
A system of data collection for factories and industrial enterprises or environmental monitoring is offered. It measures specific parameters, such as temperature, humidity, level of gases present in the atmosphere, and movement of any person near the prohibited zone at a particular moment, and transmits these parameters to the control room wirelessly.Bluetooth, WSN, ZigBee[75]
Development of hardware and software for an IoT weather monitoring system based on the Arduino Mega2560 board, digital pressure, temperature and humidity sensor BME280, and Wi-Fi module ESP-01 built on the ESP8266 chip.ThingsBoard IoT, MQTT, Node-RED, Wi-Fi[76]
Development and implementation of a LoRa-based IoT system to monitor five dynamic parameters, including air temperature and humidity, soil temperature and moisture, and soil pH.IoT, LoRa, Wi-Fi, ThinkSpeak[77]
Research on the development and laboratory testing of imitation and physical models of a computerised system for monitoring and controlling microclimate parameters in industrial greenhouses.Proteus[78]
Testing and modelling an automatic plant irrigation system based on an Arduino microcontroller with a weather monitoring system.Proteus[79]
Development of a new approach to real-time meteorological data analysis and forecasting using an integrated system based on IoT, WSNs, and ML.IoT, WSN, RNN, ANN, RF[80]
Development of a model that predicts high crop yields and precision farming.IoT, WEKA, ML[81]
Table 2. Components used to model the functional parts of the IoT system.
Table 2. Components used to model the functional parts of the IoT system.
IoT System ComponentsType of NodeProteus Library Equivalent
Temperature sensorType A, Type B, Type CDHT22
Relative humidity sensor
Precipitation sensorType A, Type B, Type CPOT-HG 10 kΩ, POWER, GROUND
Leaf wetness sensorType A, Type B, Type CWATER SENSOR, CAP 300 uF, INDUCTOR 27 uH, POT_HG 1 kΩ, GROUND, POWER
Real-time clockType A, Type B, Type CDS1307, DC Generator 5 V, GROUND
NRF moduleType A, Type BMODULO RX (modulo rf library), MODULO TX (modulo rf library),
GROUND, POWER,
LoRa moduleType B, Type CHC-05 based on Serial Interface
GSM shieldType CSIM900D-GREEN
Arduino Uno Rev3Type AARDUINO UNO R3
Arduino Mega 2560 Rev3Type B, Type CARDUINO MEGA 2560, GROUND, POWER
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Diachenko, G.; Laktionov, I.; Vovna, O.; Aleksieiev, O.; Moroz, D. Computer Model of an IoT Decision-Making Network for Detecting the Probability of Crop Diseases. IoT 2025, 6, 8. https://doi.org/10.3390/iot6010008

AMA Style

Diachenko G, Laktionov I, Vovna O, Aleksieiev O, Moroz D. Computer Model of an IoT Decision-Making Network for Detecting the Probability of Crop Diseases. IoT. 2025; 6(1):8. https://doi.org/10.3390/iot6010008

Chicago/Turabian Style

Diachenko, Grygorii, Ivan Laktionov, Oleksandr Vovna, Oleksii Aleksieiev, and Dmytro Moroz. 2025. "Computer Model of an IoT Decision-Making Network for Detecting the Probability of Crop Diseases" IoT 6, no. 1: 8. https://doi.org/10.3390/iot6010008

APA Style

Diachenko, G., Laktionov, I., Vovna, O., Aleksieiev, O., & Moroz, D. (2025). Computer Model of an IoT Decision-Making Network for Detecting the Probability of Crop Diseases. IoT, 6(1), 8. https://doi.org/10.3390/iot6010008

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