Abstract:
Leaf disease is a prominent and destructive ailment that affects plants. Timely identification and early detection are crucial
for enhancing the future probability of leaf diseases that impact plants. The investigation of soybean plant leaf disease detection has
gained importance owing to its major impact on soybean growth, leading to decreased productivity and quality. The traditional
method of identifying soybean leaf diseases mostly relies on agricultural specialists, resulting in a significant amount of time being
utilized. Deep Learning (DL) models are promising techniques to identify soybean leaf disease detection. However, various ongoing
investigations are going on to achieve an effective model with efficient practical application. To address this problem, this study
proposes the use of a hybrid, smart and intelligent model based on dilation Convolutional Neural Network (CNN) to identify diseases
of soybean leaves. Selecting and designing the ideal model structure is still a difficult task, even though DL networks demonstrate
remarkable efficacy. The accuracy of plant disease detection based on leaf analysis may be improved by fine-tuning the values of the
hyper-parameter of dilation CNN. The proposed framework has been trained using a dataset of 1620 soybean leaf images that have
been divided into six different diseased groups. The Velocity Pausing Particle Swarm Optimization (VP_PSO), a well-studied
metaheuristic technique, is employed to optimize the hyper-parameters of the dilation CNN. This optimization aims to improve the
effectiveness of the dilation CNN in accurately recognizing diseases present in soybean plant leaves. The suggested hybrid model
performs better than other standard hybrid models such as classical CNN, VGG16, MobileNetV2, ResNet101, dilation CNN and
PSO_Dilation_CNN. As per the experimental research, the suggested VP_PSO _Dilation CNN model has a detection accuracy of
95.32%.
