Apple and Orange Diseases Detection Using Deep Learniing Techniques

Abstract

We know that Bangladesh is an agricultural country where almost all people are dependent on agriculture. In today's world where everyone is health conscious, the ability to identify fruits by quality is very important in the food industry. But farmers produced these fruits without the help of practical rational inventions. This can lead to financial mishaps and reduce profits for drivers. Fruit diseases currently pose many economic and environmental problems. But early detection of fruits diseases can prevent these accidents and keep farmers happy. The market sells different kinds of fruits. However, identifying the best quality fruit is a daunting task. Therefore, we developed an automated system to Detect fruits under natural light conditions that can provide a guideline to detect fruit. Based on Convolutional Neural Networks (CNN), I created an "Apple and Orange detection system" online application that can detect fruits and also determine if they have diseases. Not only images of unhealthy fruits were collected, but also images of infected fruits such as apples and oranges. In this study, we used a fully convolutional neural network (FCNN) for infection order and a convolutional neural network for birth-related neural functions. In this paper I applied different algorithm but I didn’t get my expectation result then I applied CNN which provide 82% accuracy. I think this result is helpful for our research.