INTELLIGENT FRUIT RIPENING AND HARVESTING PREDICTION SYSTEM USING CNN

Abstract

Both consumers and commercial fruit producers are concerned about the quality of their fresh fruit. The most important aspects in establishing the quality of a mango are the efficiency and speed with which its maturation stage is classified. It is required to create and apply image processing techniques for correct ripening stage classification of the distinct fresh incoming Fruit bunches. The market value and eating quality of fruit are typically affected by its ripeness. In this work, we propose a computer vision system that can automatically detect the ripeness of fruits using a deep learning technique. It all starts with building a database. Second, ripe fruit is classified and graded using a convolutional neural network-based framework that considers colour, brown spot development, and Tamura statistical texture features. CNN classifiers analyse the suggested system's results and functionality. According to the findings, the proposed method achieves the greatest recognition rate overall. The MATLAB image processing toolkit was utilised for the coding process.