A Robust Plant Leaf Disease Recognition System Using Convolutional Neural Networks

Plants are considered an energy supply to humanity. Plant diseases can damage farming, reducing harvest yields. This immediately affects farmers’ income and human health. Plant disease identification is one of the world’s most extensive challenges for farmers. Thus, leaf disease identification is vital in agriculture. Traditional disease detection approaches are difficult to detect in large numbers of plant leaf infectious illnesses. The ability to identify plant leaf diseases using images is rapidly improving. However, processing plant leaf images is difficult due to their complicated structure and shape. While modern deep learning algorithms can categorize and diagnose plant sickness, preparing plant leaf images is widely acknowledged as the most important and hardest step. Preprocessing has a big impact on the final results of deep learning. However, the proposed vision-based approaches efficiently detect and observe illness’s external aspects. We have used the New Plant Diseases Dataset in our paperwork. We proposed deep learning with a specially trained convolutional neural network (CNN), which can aid in the classification of plant leaf images. Our approach makes use of a CNN architecture that was trained on a collection of these plant leaf images. This method accurately classifies plant leaf images into 38 types of plant leaf diseases with 99.29% test accuracy, outperforming approaches previously defined as state-of-the-art.