Analyzing Computational Response and Performance of Deep Convolution Neural Network for Plant Disease Classification using Plant Leave Dataset

Computer vision plays a vital role in the area of agriculture for various applications like irrigation monitoring, animal monitoring in farm areas, plant classification, disease detection in plant leaves, and more. In agriculture, identifying diseases in the plant leaf is a leading challenge for the farmers because disease in plants reduces crop production and also affects the income of farmers. To ensure the minimum losses, an advanced monitoring system is needed to detect and classify the disease for the plant leaves. To resolve this challenge, this article proposed a method for plant leaf disease classification by adopting a slight variant of the deep convolutional network. In this experiment, we use the plant village dataset that contains five classes with 4197 training images and 430 test images. The experimental outcomes demonstrate that the proposed methodology reveals encouraging results with accuracy up to 97.90%, which outperforms other existing machine learning-based methods.