Application Of Machine Learning In The Color Sorting Of Agrucultural Products

Abstract

Machine learning is a learning field that gives computers the ability to learn without explicitly programming them. The two main components of machine learning are data and algorithms. Algorithms process data, train (train) parameters, and in this way acquire the ability to make decision-making decisions by adapting new data. Machine learning is widespread in various fields both in agriculture and in the food industry, either in terms of fruits, vegetables, finished products, cereals or meat industry. It takes a special application in machine vision, which allows machines to learn from experience, adapt to new technologies and perform human tasks. Mechanical vision provides an alternative as an automated, non-destructive and cost effective technique for achieving demands and expectations in terms of health food safety prescribed by international standards. The industry will also have a great role to play, including full digitalization and automation of production, or the networking of smart digital devices with products, machines, tools, robots and people. It is imperative to create "smart factories" where autonomous cyber-physical systems monitor physical processes and make decisions, and the ultimate goal is to increase productivity and efficiency, and therefore competitiveness in the global market. Deep Learning is an encouraging concept of artificial intelligence due to its ability to extract features from images and high precision in the field of digital image processing and thus the agricultural and food industry in the field of quality control.