PLANT DISEASE DETECTION USING DEEP LEARNING - Amrita; Sanjay Kumar Nayak; Rajiv Kumar; Sakshi Tomar (Journal of Mechanics of Continua and Mathematical Sciences)
The prime reason for human sustainability is Agriculture. With the frequent advances in technology, researchers should not forget the root and focus on improving the agriculture sector as well. A foremost challenge in the industry of agriculture is the detection of diseases in plants and its diagnosis which has gained significant attention over the past few years. Plant diseases have significantly degraded the overall food production. This is adversely affecting both the quantity and quality of products of agricultural. In this paper, several deep learning (DL) models are proposed to recognize the multiple classes of diseases present in plants from the images of leaves taken under various resolutions and different environmental conditions. Employing a Deep Convolutional Neural Network (CNN) in multi-class classification for detecting plant diseases can be beneficial in the early identification of these diseases and also in dealing with the negative impact of these diseases on agriculture. In the proposed method, five deep CNN models such as Sequential, ResNet50, InceptionV3, VGG16, and VGG19 are used. Comparative analysis of the implemented models suggested that DL helps in extracting the significant features and biomarkers related to these diseases. Based on the testing results, the VGG16 model beats other architectures in terms of training accuracy of 97.73% with validation accuracy of 88.82%.