Leveraging EfficientNet-B0 for Soybean Leaf Disease Detection: A Deep Learning Perspective

Plant diseases require quick and accurate identification methods since they significantly reduce agricultural production's output and quality. Traditional methods of plant protection, which depend on visual assessment of pests and diseases that impact soyabean plants, have long turnaround times and variable accuracy. A critical need as the demand for precision agriculture grows is the creation of quick, effective, and—above all—computer-aided disease recognition systems. This study proposed convolutional neural network-based classification model for soyabean leaf. Proposed model can detect nine different types of soyabean diseases. Our transfer-learning based model classifies soybean leaves using a Inception B0 convolutional neural network, and on unseen testing data, we obtain balanced accuracies between 91.11% and 96.44%. The result of proposed model is generated on 3000 different images of soyabean leaves and classify nine different types of soyabean diseases.