Deep Learning for Automatic Classification of Fruits and Vegetables: Evaluation from the Perspectives of Efficiency and Accuracy
Year: 2024
Type: article
Abstract: Within the agricultural domain, accurately categorizing the freshness levels of fruits and vegetables holds immense significance, as this classification enables early detection of spoilage and allows for appropriate grouping of products based on their intended export destinations. These processes necessitate a system capable of meticulously classifying fruits and vegetables while minimizing labor expenditures. The current study concentrates on developing an advanced model that can effectively categorize the freshness status of each fruit and vegetable as 'good,' 'medium,' or 'spoiled.' To achieve this objective, various artificial intelligence models, including CNN, AlexNet, ResNet50, GoogleNet, VGG16, and EfficientB3, have been implemented, attaining remarkable success rates of 99.75%, 97.97%, 96.71%, 99.49%, 98.75%, and 99.81%, respectively.
Authors: Demet Parlak Sönmez, Şafak Kılıç
Institution: Kayseri Üniversitesi
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: bronze
OpenAlex ID: W4403156253