Fruit Ripeness Assertion Using Deep Learning

Abstract

The agricultural industry is one of the most important sectors in any country because it contributes to so many different areas. In comparison to other emerging countries, however, farmers and agriculture fields in some countries have limited technology and reach. Agricultural and allied sector operations employ 54.6 percent of the total workforce and contribute for 17.1 percent of the country's Gross Value Added (GVA) in 2017-18, according to India Census 2011. However, Agriculture’s contribution to GVA continues decline.[7] This agricultural field is obviously a challenging field to the digital technology and this “smart fruit ripening assertion” model considerably gives the high-quality and accurate results by utilizing the deep learning techniques such as YoloV3 which is a deep Convolutional Neural Network (CNN). This model's main focus is on the design and implementation of practical tasks, such as predicting the ripening stages of various types of fruits based on form, colour, and texture by combining and comparing various ML methods, OpenCV, and Internet of Things (IoT), thereby providing accurate prediction of ripening stages of fruits with the aid of a computer application which results introduction of large-scale manpower and saves time.