A Deep Learning Approach for Automated Detection and Classification of Pomegranate Fruit Disease using EfficientNet

Pomegranate is one of the most well liked and nutritious fruits in the world. Pomegranates are now processed into a variety of food products and nutritional supplements due to the growing demand for them. Nowadays, pomegranate fruits are affected by diseases. In this paper, an Efficient Net classification is proposed to quickly identify diseases in pomegranate fruit. Firstly, histogram equalization is applied to the pomegranate fruit disease dataset to modify an image’s pixel values based on its intensity and by using laplacian filtering edges are detected to get high quality image. Next, the processed image is given to the K Means clustering for the separation of relevant regions. After that pomegranate fruit disease is featured using Scale Invariant Feature Transform (SIFT) for further analysis. Finally, Efficient Net framework is processed to enhance the classification of pomegranate fruit disease. Using python software the proposed framework is examined and the developed classifier results with accuracy of 95% in detecting disease when compared to other techniques.