Apple Disease Classification Built on Deep Learning

In horticulture and agriculture, classification and intelligent identification of disease is the need of the hour. Apple is one of the most cultivated and consumed fruit in the world. It is a nutritious fruit that contains vitamin A, B1, B12, and folic acid. It is a fruit that can even keep the doctors away. The same fruit is susceptible to various diseases such as blotch and nematode, leading to social and economic losses. This paper proposes the classification of diseased and healthy apples using the deep learning technique. A dataset of 570 images of 3 Apple diseases (Blotch, Rot, and Scab) are used in the model along with normal apple images. Further data is augmented and the state-of-the-art convolution neural network, ResNet 50 is utilized for the multiclass classification and prediction model. Then performance is measured via accuracy with the help of performance matrices and the result is analyzed with its future scope.