Today, smart agriculture is one of the core technologies for sustainable development and increasing the efficiency of open-field crop production enterprises of various sizes and forms of ownership in the face of changing climate conditions. The development and implementation of computerized methods and intelligent software and hardware solutions for transforming large volumes of agroclimatic data distributed in time and space is a relevant and important field for improving the efficiency of information technologies for agrotechnical applications. In this article, the scientific and applied problem of creating and validating a computerized method for predicting the probability of occurrence of crop diseases at the pre-symptomatic stage, which forms the basis of software and hardware components for processing data from agromonitoring systems based on fog architecture, has been solved. The main results of the research are: reduction of the number of informative features to five based on the Harris Hawk Optimizer algorithm, proving the effectiveness of Bagged Trees and Medium Neural Network algorithms in the classification of Powdery Mildew in Wheat, synthesis and testing of a computer model in Simulink that implements a full cycle of transformation of agroclimatic monitoring data in predicting the Risk of Powdery Mildew in Wheat. In addition, prospective directions for further research to improve the efficiency of information technologies for predicting the probability of crop diseases are substantiated in the article.
To date, the principles of digitalization and intellectualization of technological processes are one of
the global trends in improving the efficiency of production processes at enterprises of various
profiles, scales and forms of ownership. Agriculture is one of the strategic sectors of the national
economies of many countries, and therefore requires constant search and implementation of
scientifically substantiated approaches to the sustainable development of agricultural practices.
Smart farming is a key concept relevant to running production processes in agricultural enterprises,
particularly open-field crop production. This concept, in turn, is made possible by introducing
technologies such as: Internet of Things, machine learning and artificial intelligence, remote sensing,
drones and robotics. This approach allows achieving a significant socio-economic, environmental
and technological effect, which consists in: efficient use of material, land and labor and time
resources; increasing the resistance of field crops to changing climatic conditions; increasing yields
and minimizing negative environmental impact [
Based on a priori analysis of current statistics on agricultural practices at the global level, it has
been established that cereals (wheat, rice, corn, barley, and others) are the most popular crops in
terms of cultivated areas and specific yields [
Therefore, an actual scientific and practical task is to develop and implement methods, models, and software and hardware for predicting the occurrence of crop diseases at the pre-symptomatic stage in real time, which will allow timely planning and implementation of agrotechnical measures to increase the stress resistance of crops and preserve the harvest in changing agroclimatic conditions.
In the present-day world practice, there is a significant number of high-quality research and
developments of information and computer technologies for agrotechnical purposes to detect the
parameters and characteristics of the processes of occurrence and progression of crop diseases based
on various data collection technologies [
One of the main tasks, which is the focus of many relevant studies in developing intelligent
information technologies for predictive monitoring of crops, is the precise and reliable analysis of
observation results. To date, machine learning methods have gained considerable popularity in
solving problems of intellectual analysis of large amounts of data when creating intelligent
information technologies for various applied fields [
The perspectives of integrating sensor networks for agroclimatic monitoring and machine
learning methods have been proven by the authors of scientific studies on: the introduction of
precision farming systems [
Thus, the results of the analysis of the current state of scientific and applied research prove the potential and effectiveness of creating and implementing information technologies for predictive monitoring of crop diseases based on online measurements of soil and climatic parameters with their subsequent processing by software based on machine learning algorithms. Consequently, the current research task is to develop computer components for complex intelligent processing of agromonitoring measurement data that implements a full cycle of data transformation (primary statistical processing, selection of informative features, and predictive analytics) within an integrated hardware and software architecture, taking into account the specifics of detected diseases and agroclimatic growing conditions for specific types and periods of grain crops.
Therefore, the aim of the article is to further develop information technologies for agrotechnical purposes to predict the risk of occurrence of grain crop diseases (Powdery Mildew Blumeria graminis in wheat) at the pre-symptomatic stage through the development and research of computerized method of intelligent analysis of measured data of agroclimatic monitoring utilizing machine learning models. The object of research is information processes of software analysis of agroclimatic data distributed in time and space. The subject of research is methods and computer models of complex predictive processing of agroclimatic monitoring data.
Accordingly, researching the development and validation of computerized methods and software components of predictive transformation and analysis of agroclimatic measurement data during the creation of information technologies for agrotechnical purposes to increase the stress resistance of grain crops is an actual scientific and practical task.
Two primary software environments were used in this research: Python 3.10.12 and MATLAB R2024a. Preliminary data analysis was performed in Python using libraries such as Pandas and NumPy. MATLAB was used to train the classifier model with the possibility of further generating code for microcontrollers. For this purpose, the Simulink and Statistics and Machine Learning toolboxes were used.
In Figure 1, a generalized structure of the research in this article is illustrated.
The data for the research was obtained using professional Metos weather stations from Pessl Instruments via the FieldClimate IoT platform, access to which was provided by Metos Ukraine LLC. The experimental data reflects the results of monitoring soil and climatic parameters collected in two agroclimatic zones of Ukraine from September 2022 to September 2023: northern steppe of Ukraine: a zone characterized by an arid and very warm climate. The hydrothermal coefficient varies from 0.7 to 1.0, and the annual sum of temperatures ranges from 2900 °C to 3300 °C; forest-steppe of Ukraine: insufficiently humid and warm zone with a hydrothermal coefficient of 1.0 to 1.3 and an annual sum of temperatures from 2500 °C to 2900 °C.
The data sample from the northern steppe zone (Dnipro region) consisted of 8656 records with a sampling interval of 1 hour and 14 attributes. Similarly, the data sample from the forest-steppe zone (Cherkasy region) consisted of 8687 records with the same sampling interval and number of attributes. Both samples have the probability of occurrence of Powdery Mildew Blumeria graminis as the target function for further analysis and modeling. A description of all attributes included in the two data sets is given in Table 1.
During the preliminary data analysis, all rows containing missing values were removed to avoid distortion of the results and to ensure the correctness of the modeling. There were two such rows and given that ‘PMBG’ is calculated once a day, their removal does not affect the value of the target function. In addition, for the initial analysis of the data in Python, the describe() function from the Pandas library was used to obtain statistical information about the numerical characteristics of the data, such as mean, standard deviation, minimum and maximum values, and quartiles. These indicators allowed assessing data distribution and identifying possible anomalies and general trends in the dataset. The statistical indicators obtained as a result of using describe() for the combined sample from the two regions are shown in Table 2.
The average ‘AT’ value of 10.6 °C indicates a moderate climate in the region. The range of values from 10.9°C to 37°C indicates the presence of both cold and very warm periods. The ‘AT’ values are centered around 9.8°C (median), with the bottom quartile (25%) falling within 2.7°C and the top quartile (75%) falling within 18.2°C, indicating a significant temperature variation. The average ‘RH’ value is 72.6%, indicating generally humid conditions. The humidity varies widely, with a minimum of 19% and a maximum of 100%, with most values concentrated between 61% and 87%. A mean ‘PR’ value of 0.1 mm indicates low precipitation. In fact, most of the records show no precipitation, as the median and 25th quartile are 0. The maximum value of 15.2 mm indicates significant but rare precipitation. The average ‘LW’ value is 8.6 minutes per hour, indicating predominantly dry conditions. 75% of the ‘LW’ values are 0, which means that the leaves remain dry most of the time. 75% of ‘ET’ values below 0.1 mm indicate generally low evapotranspiration. The average ‘PMBG’ value is 12.2%, indicating a low average risk of developing the disease. The maximum ‘PMBG’ is 70%, which indicates a significant risk in certain periods. At the same time, the median of 0% indicates that a significant part of the sample had no or low risk of the disease, but the 75th quartile at 20% shows that there are periods with an increased risk of developing the disease.
The data in Table 2 shows moderate climatic conditions with relatively low precipitation and moderate temperatures. The risk of developing Powdery Mildew is generally low, although periods of higher risk require attention.
The risk of Powdery Mildew varies by 10% on average during the day under favorable conditions
[
Taking into account the above transformations, the columns of the final table are renamed according to the predefined names stored in the variable INPUT_SUMMARY_COLUMNS = [‘AT_mean’, ‘FVT12S’, ‘TL16S’, ‘TG21S’, ‘FVT16S’, ‘TG25S’, ‘DP_mean’, ‘SR_sum’, ‘SR_count’, ‘VPD_mean’, ‘RH_mean’, ‘RHG85S’, ‘PR_sum’, ‘LW_count’, ‘WS_mean’, ‘WD_mean’, ‘WG_mean’, ‘ST_mean’, ‘ET_sum’, ‘PMBG_mean’], and the calculations correspond to the Python code (see Appendix A). The difference between the value of the disease risk for the previous day and the current day is calculated using diff(). Then, based on this difference, a new attribute ‘PMBG_class’ is created in which the class of risk change is stored: 1 – risk has increased, 2 – risk has decreased, 0 – risk has not changed.
The data was then split into training and test samples in a 70:30 ratio. 70% of the data was used to train the models, and 30% to evaluate their performance.
After performing these steps, the imbalance of classes in the target variable of the training dataset
was detected (class 1 – 6.6%, class 2 – 6.4%, class 0 – 87%). This can lead to the model learning to
favor a more common class, ignoring important features of less represented classes, which ultimately
degrades the overall performance of the classifier model. Two approaches were used to address this
problem: ‘undersampling’ and ‘oversampling’. The ‘undersampling’ approach is about reducing the
number of instances of the majority class to achieve a balance with the minority. This allows the
model to better account for the minority, as the number of samples of all classes becomes
proportional. To implement this approach, the pandas.DataFrame.sample() function was used, which
allows randomly selecting instances from the majority class. Figure 2(a) shows a comparison of the
original dataset before dividing it into training and test samples and the data after resampling. The
second approach is ‘oversampling’, which implies increasing the number of minority samples to
achieve balance with the majority. One of the most common methods for this is the Synthetic
Minority Over-sampling Technique (SMOTE) [
original sampled original train smote (a) (b) Figure 2: (a) comparison of original and resampled preprocessed dataset across three categories, (b) comparison of original train data and resampled train data with SMOTE across three categories.
These two approaches help to achieve a balance in the data and improve the overall quality of the model, preventing it from being biased towards the class with more instances and increasing classification accuracy for less represented classes.
The next step in the process of preparing data before training classification models is scaling, including standardization and normalization. Scaling is an important step in data processing because different features can have different scales, which can negatively affect the performance of machine learning models, especially those based on distance or gradient methods.
The mean and standard deviation are computed from the training data, and these values are then
used to standardize the training data. The same mean and standard deviation from the training set
are also applied to standardize the test data. This ensures that both datasets are transformed
consistently, allowing for accurate evaluation of the performance of the model. The standardization
is performed in MATLAB Classification Learner App [
The last step is to extract significant features. To do this, the 'Feature Selection Wrapper Class'
algorithm from a GitHub toolbox was used [
The algorithm proposed by Heidari and co-authors [
This algorithm has a high convergence rate and excellent local search capabilities, which makes it effective for feature selection problems. In this research, the HHO algorithm proposed five significant features (‘TL16S’, ‘FVT16S’, ‘DP_mean’, ‘ST_mean’, ‘ET_sum’), which were extracted for further analysis and model training.
After feature selection, the training set was used to train various Simulink-compatible
machinelearning classifiers. Types of classifiers chosen and their relevant hyperparameters [
Logistic Regression Efficient Linear SVM
SVM SVM SVM KNN KNN KNN KNN KNN KNN Medium SVM Coarse SVM Fine KNN
Cosine KNN Cubic KNN Weighted KNN
Based on the described methodology for assessing the risk of Powdery Mildew Blumeria graminis in wheat, the research steps of this article were presented in the form of a structural algorithmic scheme, as shown in Figure 3.
When training the models, the five-fold cross-validation methodology was used to assess their
performance [
The data is divided into five subsets of equal size. At each iteration, four subsets are used to train the model, and one is used to test it. As a result, average accuracy rates are obtained, which gives a more stable and reliable assessment of model quality on different chunks of the dataset.
After training and testing the classification models, the following results were obtained, as shown in Table 4. For each model, training was performed using undersampling and oversampling data balancing approaches, in particular, the accuracy rates on the validation (Val.) and test (Test) datasets were compared. 68 75 48 80 71 70 75 69 69 69 58 63 65 65 63 63 58 70 56 60 86 89 84 88 83 88 91 90 90 89 72 72 72 74 75 73 80 78 71 76
The tree models (Fine Tree, Medium Tree, Coarse Tree) showed the best accuracy when using oversampling, with the best accuracy for Fine Tree (84% on validation and 73% on testing). Coarse Tree significantly reduced the accuracy on the undersampled test set (56%). SVM models had stable results, especially when using oversampling. For example, Quadratic SVM and Cubic SVM demonstrated high accuracy rates (up to 90% on the validation set). Fine KNN and Weighted KNN achieved the best results in oversampling, showing 90% and 89% accuracy, respectively, on validation. Medium and Wide Neural Networks showed the highest accuracy (91% and 90%, respectively) with oversampling, indicating their ability to efficiently process more balanced data. The tree-based methods (Boosted Trees, Bagged Trees, RUSBoosted Trees) also performed well, especially Bagged Trees, with 88% accuracy on the validation and 74% on the oversampled test set. Thus, the use of oversampling generally improved the performance of the models, especially for SVMs, KNNs, and neural networks.
To analyze the performance of the models in more detail, the Confusion Matrix for the training and test data, as well as the ROC curves for the three classes, were constructed, as shown in Figure 4 and Figure 5. This analysis was performed for two models: Bagged Trees (on undersampled data) and Medium Neural Network (on oversampled data).
The Confusion Matrix for both models showed similar results. For the Bagged Trees model on the undersampled data, it can be seen that the model generally copes with the classification of classes, although there are some errors in the classification of smaller classes. The Medium Neural Network model, on the other hand, showed better results on oversampled data, reducing the number of misclassifications, especially for less represented classes.
The ROC curves for both models show high sensitivity and specificity for each of the three classes. Both models perform well for most classes, with the Medium Neural Network on oversampled data showing slightly higher performance in terms of area under the curve (AUC) for class ‘2’. 0 e0.8 t a iiteR0.6 v s o P0.4 e u r
T0.2 label score ClassificationEnsemble Predict
To Workspace
An example of the model output in Figure 6 for the northern steppe scenario in the form of time graphs is shown in Figure 7, comparing actual and predicted data on the risk of Powdery Mildew Blumeria graminis in wheat.
The Bagged Trees model trained on undersampled data was then exported [
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The analysis of time graphs shows that the predicted data from the model is able to reproduce the main trends of growth and reduction of disease risk in the relevant time periods. Although there are some discrepancies between the actual and predicted values at certain points, in general, the model reflects risk behavior well and can be used for operational monitoring and decision-making in openfield conditions.
In this research, the relevant scientific and practical task has been solved by developing and researching the computerized method for predicting the risk of powdery mildew in wheat based on software analysis of soil and climatic monitoring data. The research allowed for the further development of information technologies for agrotechnical purposes to predict the risk of occurrence of grain crop diseases (Powdery Mildew Blumeria graminis in wheat) at the pre-symptomatic stage. The main results of the research include: 1. Using undersampling and oversampling methods to solve the problem of class imbalance in the training sample. 2. The application of feature selection algorithms, in particular Harris Hawk Optimizer, reduced the number of features to five. 3. Classification models such as Bagged Trees and Medium Neural Network performed well on both validation and test datasets, demonstrating good generalizability. 4. The export of the Bagged Trees model to the Simulink environment and the subsequent generation of program code for microcontroller devices allows it to be used for real-world prediction and control in agroclimatic systems based on fog architecture.
To improve the results, future research should pay attention to the following aspects: 1. Improving the parameters of algorithms such as Bagged Trees and Neural Networks by further tuning hyperparameters, which can lead to even more accurate results. 2. Involvement of new data sources, such as information on field cultivation, which can improve the recognition of conditions that contribute to the occurrence and development of diseases. 3. Further integration of the model with IoT platforms for automatic real-time monitoring can provide more up-to-date and accurate data for predicting disease risks.
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’ funded by the Ministry of Education and Science of Ukraine at the expense of the state budget (state registration number 0124U000289).
The authors have not employed any Generative AI tools. Appendix A. Python code for daily summarized data def read_df_with_daily_summary(csv_path: str) -> pd.DataFrame: df = pd.read_csv(csv_path) df["DT"] = pd.to_datetime(df["DT"], dayfirst=True) df.set_index("DT", inplace=True) # Resample data by day to calculate the required values df_daily_summary = df.resample("D").agg( { ) # Reset the index for a clean DataFrame df_daily_summary.reset_index(inplace=True) df_daily_summary.drop(columns=["DT", "PMBG_mean"], inplace=True) return df_daily_summary