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

Abstract

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. 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.