Fruit Detection and Three-Stage Maturity Grading Using CNN

Abstract

Agriculture is a major sector for economic growth and development. The cultivation of fruit crops is a part of agriculture thus helping in the prosperity of our nation. In recent years, there has been a sudden hike in health problems and therefore, it has led to increasing demand for fruits and vegetables. Therefore, the use of innovative technologies is of significant importance for the fruit sector to give ripe and fresh fruits. Currently, Artificial Intelligence is a technology that is transforming every line of work. Particularly, Deep Learning (DL) has diverse applications due to its potential to learn mighty representations from images. A Convolutional Neural Network (CNN) is a noteworthy class of Deep Learning architecture that is built with the capability to bring out distinctive characteristics from image data. The utmost concern of many customers, vendors, and farmers is the quality of fruits and vegetables produced. Differentiating the fruits according to their ripening stages is the most crucialfactor in regulating the quality of fruits. This work used a high-quality dataset with 9997 images comprising 15 fruit classes. Moreover, based on the significant applications that Convolutional Neural Networks have had till now, it proposes an analysis of deep learning algorithms for fruit detection and three-stage maturity grading and achieves 90.24 percent accuracy. The results obtained will help in the development of fast and accurate detection of fruits and their quality