Plant Disease Detection using Convolutional Neural Networks (CNN) Algorithm

In countries like India, substantial crop losses happen every year due to the delayed identification of plant diseases. The more efficient approach is presented by quick and accurate disease identification, which decreases the need for subject matter experts. It can be challenging to differentiate between various illnesses of plants based on leaf observation, particularly when similar textures are observed. This study focuses on classifying and predicting plant illnesses from photos using deep learning methods like convolutional neural networks. CNN performs direct image processing. The primary machine learning models used in this study are convolutional neural networks (CNN). The agriculture output of the country is impacted by infected plants and crops. To identify and diagnose diseases, farmers or specialists usually keep an eye on the plants. However, this procedure is frequently costly, consumes more time which is not precise. One method of identifying plant illnesses is to look for a spot on the leaves of the affected plant. This paper's goal is to create a disease recognition model that is backed by the classification of leaf images. Convolution neural networks (CNNs), a kind of artificial neural network made especially to handle pixel input and utilised in image identification, are being employed in conjunction with image processing to accomplish this.