Open AccessA Survey on Fresh Produce Grading Algorithms Using Machine Learning And Image Processing Techniques
Author(s) -
T K AmeethaJunaina,
B. Ebenezer Abishek,
Vr Rajendren,
Mohammed Shakir,
Prince Kumar
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/981/4/042084
Subject(s) - profitability index , computer science , grading (engineering) , automation , supply chain , agriculture , image processing , quality (philosophy) , artificial intelligence , marketing , engineering , business , image (mathematics) , mechanical engineering , ecology , civil engineering , finance , biology , philosophy , epistemology
Agriculture has been the backbone of the Indian economy. Automation in the agriculture field helps to improve productivity and economic growth. The export market for fresh produce requires fast and reliable fruit and vegetable quality detection techniques. The traditional manual system of quality assessment is a time consuming and tedious task which is more prone to error. The goal of supply-chain management is to add product value by maintaining quality, reduce wastage of fresh produce, retain consumers by keeping customer satisfaction, and increase the profitability. Researches are going on in various domain to develop a fast and reliable automated fruit quality grading system which helps to meet the food value goals of supply-chain. This paper focuses on a detailed survey of the researches being carried out on various techniques used in post-harvest grading of fresh produces using computer vision, image processing and machine learning techniques. Few papers have been reviewed and discussed here pertaining to the above techniques. The advantages and shortcomings of various methods are also mentioned. This comprehensive paper will give researchers a deeper insight to the state of the art technologies in fresh produce grading system.