Apple Disease Diagnosis for Precision Agriculture Using Deep Learning Neural Network

Abstract

Fruit diseases in agricultural products could result in financial loss. A human specialist could classify diseases, which is the traditional method, but it is costly and time-consuming. In this paper, we focus on an important fruit—apples. This paper proposes using a deep-learning neural network to diagnose Apple Fruit Disease in precision agriculture. The approach involves collecting a large dataset of images of healthy and diseased apple fruit, preprocessing the images, and training a neural network model to classify the images into their respective categories. The model's accuracy is evaluated using a testing set, and the trained model can be deployed through a web application or mobile app for use by farmers in the field. The proposed approach has the potential to improve crop yield and ensure food security by enabling early identification and prevention of disease spread.