YOLOv7: A deep learning approach for tomato classification

Abstract

Artificial intelligence and machine learning are expanding and being used extensively in many facets of life these days. Large-scale data processing, data-driven learning, and automated decision-making are all possible with artificial intelligence. The tomato recognition model achieved an accuracy of 93.3 % for normal tomatoes and 89.1 % for damaged tomatoes on the test dataset. Throughout the investigation, the group used the YOLOv7 algorithm for computer-visual tomato recognition. This allowed for evaluation and analysis based on the data gathered on tomatoes. To boost output and improve product quality in Vietnam's agricultural sector, the research's findings can be used to classify tomatoes in the most precise manner.