A Novel Model to Detect and Classify Fresh and Damaged Fruits to Reduce Food Waste Using a Deep Learning Technique. (23rd May 2022)

• Record Type: Journal Article
• Authors: Kumar, T. Bharath; Prashar, Deepak; Vaidya, Gayatri; Kumar, Vipin; Kumar, S. Deva; Sammy, F.
• Other Names: Khan Rijwan Academic Editor.
• Is Part Of: Journal of food quality. Volume 2022(2022)
• Publication Date: 2022-05-23
• Subjects: Food industry and trade -- Quality control -- Periodicals; Food industry and trade -- Standards -- Periodicals; Food -- Periodicals; 664.07
• DOI: 10.1155/2022/4661108
• Languages: English
• ISSNs: 0146-9428

Abstract

Abstract : Due to a lack of efficient measures for dealing with food waste at many levels, including food supply chains, homes, and restaurants, the world's food supply is shrinking at an alarming pace. In both homes and restaurants, overcooking and other factors are to be blamed for the majority of food that is wasted. Families are the primary source of food waste, and we sought to reduce this by identifying fresh and damaged food. In agriculture, the detection of rotting fruits becomes crucial. Despite the fact that people routinely classify healthy and rotten fruits, fruit growers find it ineffective. In contrast to humans, robots do not grow tired from doing the same thing again and again. Because of this, finding faults in fruits is a declared objective of the agricultural business in order to save labour, waste, manufacturing costs, and time spent on the process. An infected apple may infect a healthy one if the defects are not discovered. Food waste is more likely to occur as a consequence of this, which causes several problems. Input images are used to identify healthy and deteriorated fruits. Various fruits were employed in this study, including apples, bananas, and oranges. For classifying photographs into fresh and decaying fruits, softmax is used, while CNN obtains fruit image properties. A dataset from Kaggle was used to evaluate the suggested model's performance, and it achieved a 97.14 percent accuracy rate. The suggested CNN model outperforms the current methods in terms of performance.