Plant Leaf Diseases Identification in Deep Learning

Abstract

Crop diseases constitute a big threat to plant existence, but their rapid identification remains difficult in many parts of the planet because of the shortage of the required infrastructure. In computer vision, plant leaf detection made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. employing a public dataset of 4,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to spot one crop species and 4 diseases (or absence thereof). The trained model achieves an accuracy of 97.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of coaching deep learning models on increasingly large and publicly available image datasets presents a transparent path toward smartphoneassisted crop disease diagnosis on a large global scale. After the disease is successfully predicted with a decent confidence level, the corresponding remedy for the disease present is displayed that may be taken as a cure.