Crop disease detection using deep learning techniques on images

Abstract

Agriculture is a field which is referred to as the main sector for the development of the economy in various countries, and it is also providing food to the large population of the world despite various limitations and boundaries. Food security is threatened by several factors including climate change, the decline in pollinators, plant diseases and others. Different efforts have been developed to prevent crop loss due to infections in the plants. The advancement in technology is helping farmers in developing different systems that can help in reducing the problem. Smartphones specifically offer very novel ways to identify diseases because of their computing power, high resolution displays, and extensive built-in sets of accessories, such as advanced HD cameras. This leads to a situation where disease diagnosis based on automated image recognition is needed. Image recognition is made possible by applying a deep learning approach. So the research is aimed to analyze deep learning-based image detection techniques to identify the various diseases in the plants. The “PlantVillage” dataset has been used to train models. Deep learning Architectures such as AlexNet and GoogleNet, ResNet50 and InceptionV3 are used. Two approaches are used to train the model: ‘training from scratch’ and ‘transfer learning’. It was found from the results of the primary analysis that the GoogleNet leaves behind the AlexNet, ResNet50 and InceptionV3 in training from scratch approach. And ResNet50 performed best in transfer learning.