Background: Soybean, a vital global crop, faces threats from diverse leaf diseases impacting yield and quality. By utilizing cutting-edge Machine Learning-Convolutional Neural Network (CNN) models, this study develops a Smart Detection System for the precise identification of soybean leaf diseases. 
 
Methods: Convolutional Neural Networks (CNNs) are used in this study to identify soybean leaf diseases. Different images that depict different diseases are used to train the CNN model. The dataset obtained from Mendeley includes three essential categories: Diabrotica Speciosa, Caterpillar and Healthy soybean leaves. Labeling, grayscale conversion and scaling are all part of image processing.&nbsp;80% of the dataset is used for training, 20% is used for validation and the accuracy of the model is assessed. 
 
Result: The CNN model showcases exceptional capabilities, achieving an impressive 95 per cent accuracy in precise soybean leaf disease classification. The Smart Detection System emerges as a powerful and timely tool for disease identification, holding significant implications for advancing precision agriculture. This study underscores the transformative potential of advanced machine learning in reshaping sustainable soybean crop management practices.

Soybean Leaf Disease Identification Through Smart Detection using Machine Learning-convolutional Neural Network Model

Background: Soybean, a vital global oilseed crop, faces significant challenges from pest infestations and related damage that compromise productivity. Early and accurate detection of pest damage is critical for sustainable agriculture. This study evaluates the effectiveness of the ResNet-20 architecture in identifying and classifying soybean leaf conditions into three categories-Healthy, Caterpillar-damaged and Diabrotica speciosa-affected-and assesses its potential for practical agricultural deployment. 
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Methods: The study utilized a dataset of 6,410 soybean leaf images sourced from the Mendeley database, categorized into three classes. The ResNet-20 model architecture incorporated convolutional layers, residual connections and global average pooling for efficient feature extraction and classification. Model performance was evaluated using metrics such as precision, recall, F1-score, accuracy and the Matthews Correlation Coefficient (MCC). 
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Result: ResNet-20 demonstrated robust performance with an overall classification accuracy of 86.58%. High precision and recall values were observed for pest-affected categories, although a reduced recall for the &ldquo;Healthy&rdquo; class was noted. The MCC of 0.7728 highlighted strong predictive reliability. Misclassifications were attributed to visual similarities between pest-related symptoms and class imbalance in the dataset.

Deep Learning-driven Precision Agriculture: Application of ResNet-20 for the Early Detection and Classification of Soybean Insect Pests under Real-world Constraints

Background: Agricultural specialists usually examine leaves closely to check for plant diseases, that process takes a long time and can have errors. To overcome these problems, nowadays, machine learning methods, specifically sequential convolution neural networks (CNN) are widely used, because of their extensive potential to extract features and patterns from the image dataset.&nbsp; 
 
Methods: This study introduces a complete methodology for detecting diseases in soybean plants by employing Convolutional Neural Networks (CNNs). A dataset sourced from the Mendeley database, containing images of soybean legume crops affected by caterpillars, Diabrotica Speciosa diseases and undamaged (healthy) foliage is used., The research explores the efficacy of the CNN Model in accurately classifying these diseases. The CNN algorithm is trained in such a way that it can handle the complex nature of soybean leaf imaging by utilizing convolutional layers for extracting features, pooling layers for reducing dimensionality and softmax layers for classification.&nbsp; 
 
Result: The training and validation results exhibit remarkable accuracy rates (97.64% accuracy) after 150 epochs. The evaluation metrics, such as precision, recall and F1-score, demonstrate the model&rsquo;s performance across different leaf diseases of soybean, suggesting its ability to accurately identify instances inside each category. The classification matrices provide complete knowledge of the accuracy of the prediction of different diseases. The overall accuracy of the proposed model is 94.05%. This study can be utilized as a reference to increase the progress of agricultural disease detection, in turn, can enable enhance the reduction of crop losses and ensure food security.

Soybean Leaf Disease Identification Through Smart Detection using Machine Learning-convolutional Neural Network Model

Deep Learning-driven Precision Agriculture: Application of ResNet-20 for the Early Detection and Classification of Soybean Insect Pests under Real-world Constraints

Early Leaf Disease Detection of Soybean Plants using Convolution Neural Network Algorithm