Comparative Evaluation of the Convolutional Neural Network based Transfer Learning Models for Classification of Plant Disease

The wide scale prevalence of diseases in agricultural crops affects both the production quality and quantity of agricultural products at local to regional scale. More often than not, the diseases remain unidentified causing huge distress to the farmers while threatening national food security. In order to circumvent this problem, early diagnosis of diseases using a fast and reliable method is beneficial. Plant disease identification from images captured by digital cameras is an area of active research. Use of various machine learning algorithms for plant disease classification and the evolution of deep convolutional neural network (CNN) based architectures have further enhanced the plant disease classification accuracy. In this context, an automated computer vision-based plant disease detection and classification scheme from plant and leaf’s photographs will be highly desirable. Although, there exist a few techniques currently used in an adhoc fashion for plant disease detection and/or classification, a systematic study to evaluate their usage and efficacy on actual plant data has largely remained unexplored.The aim of this paper is to evaluate various CNN based state-of-the-art transfer learning architectures like GoogLeNet, AlexNet, VGG16 and ResNet50V2 models for plant disease detection and classification. The models were tested on popular publicly available three plant disease benchmark database such as PlantVillage Dataset, New Plant Disease Dataset and Plant Pathology Dataset. Various validation metrics such as Precision, Recall, F1 score and overall accuracy were used to evaluate the final results of the experiments, which revealed that VGG16 rendered highest accuracy of 96.6%, 98.5% and 89% on the three dataset respectively, outperforming all other state-of-the-art models.