Detection of Healthy and Diseased Plant Leaf Based On Alexnet-Convolutional Neural Network Using Deep Learning

In current years, the most widely used image processing is to analyze and detect plant leaf diseases. Furthermore, using image processing has an important effect on crop yield and plant leaf quality. This is an important process in addressing the problem of plant leaf diseases in agriculture. Therefore, image processing can enhance the effectiveness and exactness of plant disease detection. However, the detection of plant diseases faces significant challenges such as time consumed, processing and large amounts of data. Therefore, the existing method is inefficient and has low accuracy in detecting plant leaf diseases. To solve this problem, the proposed A-CNN (Alexnet-Convolutional Neural Network) technique is applied to accurately identify healthy and unhealthy plant leaf. Moreover, we utilize HEMF (Histogram Equalization Mean Filter) technique for pre-processing to eliminate noise in plant leaf images, reduce background noise, and enhance image quality. Afterwards, features can be selected using the Enhanced Particle Swarm Optimization (EPSO) algorithm to identify disease behaviour in plant leaves. Finally, the A-CNN method proposed can be utilized to identify healthy and diseased parts in plant leaves using Deep Learning (DL), resulting in enhanced accuracy. Moreover, test results can be evaluated using accuracy, sensitivity, specificity, and accuracy based on the offered technique. Thus, the experimental effects show that the proposed ACNN technique can detect plant leaf diseases effectively.