Plant Leaf Diseases Detection Using Deep Learning Algorithms

Abstract

Listen

Translate article

AbstractA key difficulty in agriculture are plant leaf diseases and destructive insects. The development of early treatment options for leaf diseases should be aided by faster and more exact prediction of leaf illnesses, while reducing economic losses. Researchers have been able to considerably improve the overall performance and accuracy of object detection and identification systems because to recent advances in deep learning. Using a deep learning methods, conventional neural network (CNN) models were constructed to identify and diagnose plant leaf dis- ease in basic images of damaged and healthy plants. This paper uses four deep learning models like AlexNet, simple sequential model, MobileNet, and Inception-v3 to detect disease in leaf. Here, new plant diseases dataset has been used for the training and testing. There are 38 distinct classes in all, including basic leaf images of healthy and diseased plants are used. Plant leaf images from the Internet are also tested using this trained model. The models successful outcome makes it an effective early warning tool, as well as a strategy that may be extended to allow a real-world integrated plant disease detection system. After evaluating all four models, we discovered that the MobileNet model is a good fit for the new plant diseases dataset, with training and validation accuracy are 99.07 and 97.52%.KeywordsConvolution neural networkDeep LearningPlant diseaseMobileNetSimple sequential modelAlexNetInception-v3