{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fruit-maturity-recognition-from-agricultural","title":"Fruit Maturity Recognition from Agricultural, Market and Automation Perspectives","arxiv_id":null,"date":"2021-11-10","proceeding":"Annual Conference of the IEEE Industrial Electronics Society (IECON) 2021 11","authors":["Koteswar Rao Jerripothula","Sarvesh Kumar Shukla","Samyak Jain","Shudhanshu Singh"],"abstract":"Motivated by the potential reduction in the required manual efforts in the fruit industry, this paper attempts to automate fruit maturity recognition. We study the problem from the agricultural, market, and automation perspectives, often taken at different points in the supply chain. Since different maturity states have different visual characteristics, an image classification technology can certainly help here. To develop fruit image classifiers, we need a feature extraction method and a learning algorithm. We use different pre-trained neural networks for effective feature extraction and employ different machine learning algorithms while carrying out bias/variance analysis of\r\nthe learned models. The analysis helps us select the best ones for each perspective under consideration. We achieve 96%, 94%, and\r\n86% accuracies on our novel dataset named RipeRaw from the agricultural, market, and automation perspectives, respectively.","url_abs":"https://ieeexplore.ieee.org/document/9589215","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9589215","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"fruit-type-maturity-state-prediction-multi-1","task_name":"Fruit-type + Maturity-state Prediction (Multi-label Classification)"},{"task_slug":"raw-vs-ripe-generic","task_name":"Raw vs Ripe (Generic)"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/fruit-type-maturity-state-prediction-multi-1","task":"Fruit-type + Maturity-state Prediction (Multi-label Classification)","dataset":"RawRipe Dataset","model":"VGG16 + Logistic Regression","rank_in_archive_order":1,"of":1,"metrics":{"Classification Accuracy":"0.862"},"uses_additional_data":false},{"leaderboard":"/sota/raw-vs-ripe-generic-on-rawripe-dataset","task":"Raw vs Ripe (Generic)","dataset":"RawRipe Dataset","model":"VGG16 + Logistic Regression","rank_in_archive_order":1,"of":1,"metrics":{"Classification Accuracy":"0.944"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}