Analysis and detection of fruit defect using neural network

Abstract

Fruit quality detection is important to maintain the quality of fruits. Generally, fruit inspection is done manually which is ineffectual for farmers in the agriculture industry. This paper proposes an approach to identify fruit defects in the agricultural industry for reducing the production cost and time. Since different fruit images may have similar or identical color and shape values. Therefore, we used a method to increase the accuracy level of fruit quality detection by using color, shape and size-based method with Artificial Neural Network (ANN). ANN is a special kind of tool that is used to estimate the cost and various artificial intelligence field like - voice recognition, image recognition, robotics and much more. The fruit quality inspection acquires images as external input and then the captured image is used for detecting defects by applying segmentation algorithm. The output of the processed image is then used as input to ANN. The network uses backpropagation algorithm, was tested with training dataset and hence predicted defects with good efficiency and in much shorter time than the human inspectors. On comparing predicted and training dataset together, the feasibility of an approach reveals the efficient defect detection and classification in the agricultural industry.