Detection And Classification Of Apple Diseases

Abstract

In agricultural products, fruit diseases could lead to economic loss. In this thesis, we focus on an important fruit—apples. Disease classification could be done by a human expert, which is the old way, costs a lot of money, and is also time-consuming. Computer vision (CV) and deep learning techniques show promising results with good accuracy and less time. In this thesis, we have considered apple diseases like apple scab, apple blotch, and apple rot; these are fungal diseases. The dataset of the apples were collected from the local market; from that sample, we picked the apples which were already infected. Different models based on convolutional neural network are used for the classification. All the models showed good classification accuracy on more than 93% on testing images. The best accuracy was achieved by model-5; it gave 99.38%.