Performance Analysis Of Fine-Tuned Convolutional Neural Network Models For Plant Disease Classification

Abstract

Early identification and detection of plant leaf disease is an essential requirement for sustainable agriculture and optimum yield production. In the field of Artificial Intelligence, Deep Learning has emerged as an effective computing paradigm and shows a great potential to solve many computer vision problems. Deep convolutional neural network (CNN) is one of the deep leaning architecture that proposes implicit outcomes for image recognition and object detection applications. In this research, the benchmark deep CNN models are applied for plant leaf disease identification and classification. We have applied and evaluated performance of VGG 16, Inception V4 and ResNet 50 and ResNet 101. The dataset used during the study contains 38 classes and 87000 images. We have applied transfer learning for training the models and fine-tuned the pre-trained models used. After evaluating the performance, it has been found that ResNet 50 and ResNet 101 exhibit test accuracy 99.70% and 99.73% respectively, whereas Inception V4 achieved 98.36% and VGG16 reached to 81.63%. Thus, ResNet50 and ResNet101 have been appeared with promising results for plant leaf diseases identification and classification.