Grape Crop Disease Classification Using Transfer Learning Approach

Abstract

Grape is one of the important fruit crops which is affected by diseases. The advents of digital camera and machine learning based approaches have facilitated recognition of plant diseases. Convolution Neural Network (CNN) is one of the types of architecture used in deep learning based approach. AlexNet is a category of CNN which is used in this study for classification of three diseases along with healthy leaf images obtained from PlantVillage dataset. Transfer learning-based approach is used where the pretrained AlexNet is fed with 4063 images of above categories. The model achieved 97.62% of classification accuracy. Feature values from the different layers of the same network are extracted and applied to Multiclass Support Vector Machine (MSVM) for performance analysis. Features from Rectified Linear unit (ReLu 3) layer of AlexNet applied to MSVM achieved the best classification accuracy of 99.23%.