Tomatoes are vital in global diets for their nutrients like vitamin C and lycopene, but ripe ones spoil easily and unripe ones contain harmful solanine. Accurate ripeness detection is key to reducing waste and ensuring safety, while manual picking lacks efficiency and standards, driving the need for automated inspection. This study proposes a multi-task deep CNN based on YOLOv11, integrating Swin-Transformer into the backbone (inspired by RT-DETR) to enhance global information processing. New modules like ASSFHead and ENLCA are added to boost feature extraction. Experiments show the optimized model improves mAP50 by 1.6%, mAP50-95 by 0.6%, and recall by 2.2%. The achievement reduces harvesting losses, ensures food safety, offers references for other produce, and promotes agricultural intelligent detection technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Язык оригиналаАнглийский
Название основной публикацииProceedings of the Ninth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’25), Volume 2
ИздательSpringer Nature
Страницы144-154
Число страниц11
ISBN (печатное издание)9783032136114
DOI
СостояниеОпубликовано - 2026
СобытиеNinth International Scientific Conference on Intelligent Information Technologies for Industry - Сочи, Российская Федерация
Продолжительность: 5 ноя 20257 ноя 2025

Серия публикаций

НазваниеLecture Notes in Networks and Systems
Том1763 LNNS

конференция

конференцияNinth International Scientific Conference on Intelligent Information Technologies for Industry
Сокращенное названиеIITI 2025
Страна/TерриторияРоссийская Федерация
ГородСочи
Период5/11/257/11/25

ID: 151441971