Open AccessA Survey on Fruit Ripeness Classification Based On Image Processing with Machine Learning
Author(s) -
Mayuri Wankhade,
Umesh W. Hore
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-1097
Subject(s) - ripeness , image processing , automation , artificial intelligence , computer science , machine learning , machine vision , agriculture , feature extraction , ripening , agricultural engineering , engineering , image (mathematics) , horticulture , geography , mechanical engineering , archaeology , biology
Agriculture has a major role in the economic development of our country. Productive growth and high yield production of fruits is essential and required for the agricultural industry. Due to the lack of skilled workers, 30–35% of the harvested fruits is wasted. also, because of human perception subjectivity identification, classification and grading of fruits not done precisely. So, it is required to impose the automation system in the fruit industry. The machine learning techniques with adequate concepts of image processing have a great scope to provide intelligence for designing an automation system to differentiate the fruits according to its type, variety, matureness and intactness. Application of image processing has helped agriculture to improve yield estimation, disease detection, fruit sorting, irrigation and maturity grading. Image processing techniques can be used to reduce the time consumption and has made it cost efficient. In this paper, an automatic system is reviewed to identify the ripening stages of fruit from images. Various feature extraction is performed using different algorithm to get the low to high level features automatically and later classification is carried out using various machine learning algorithm to get ripening stages of fruit as predicted output.