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Multi-task Deep Convolutional Neural Network Based on YOLOv11 for Tomato Fruit Ripening Detection. / Wang, Q.; Fu, G.; Li, Z.

Proceedings of the Ninth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’25), Volume 2 . Springer Nature, 2026. стр. 144-154 (Lecture Notes in Networks and Systems; Том 1763 LNNS).

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Harvard

Wang, Q, Fu, G & Li, Z 2026, Multi-task Deep Convolutional Neural Network Based on YOLOv11 for Tomato Fruit Ripening Detection. в Proceedings of the Ninth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’25), Volume 2 . Lecture Notes in Networks and Systems, Том. 1763 LNNS, Springer Nature, стр. 144-154, Ninth International Scientific Conference on Intelligent Information Technologies for Industry , Сочи, Российская Федерация, 5/11/25. https://doi.org/10.1007/978-3-032-13612-1_14

APA

Wang, Q., Fu, G., & Li, Z. (2026). Multi-task Deep Convolutional Neural Network Based on YOLOv11 for Tomato Fruit Ripening Detection. в Proceedings of the Ninth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’25), Volume 2 (стр. 144-154). (Lecture Notes in Networks and Systems; Том 1763 LNNS). Springer Nature. https://doi.org/10.1007/978-3-032-13612-1_14

Vancouver

Wang Q, Fu G, Li Z. Multi-task Deep Convolutional Neural Network Based on YOLOv11 for Tomato Fruit Ripening Detection. в Proceedings of the Ninth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’25), Volume 2 . Springer Nature. 2026. стр. 144-154. (Lecture Notes in Networks and Systems). https://doi.org/10.1007/978-3-032-13612-1_14

Author

Wang, Q. ; Fu, G. ; Li, Z. / Multi-task Deep Convolutional Neural Network Based on YOLOv11 for Tomato Fruit Ripening Detection. Proceedings of the Ninth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’25), Volume 2 . Springer Nature, 2026. стр. 144-154 (Lecture Notes in Networks and Systems).

BibTeX

@inproceedings{9abf3ade18c447aca7970b92964efcc0,
title = "Multi-task Deep Convolutional Neural Network Based on YOLOv11 for Tomato Fruit Ripening Detection",
abstract = "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. {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.",
keywords = "Deep convolutional neural network, Swin-Transformer, Tomato ripeness detection, YOLOv11, Error detection, Food safety, Convolutional neural network, Lycopenes, Multi tasks, Network-based, Reducing waste, Swin-transformer, Tomato fruit ripening, Vitamin C, Fruits",
author = "Q. Wang and G. Fu and Z. Li",
note = "Export Date: 29 March 2026; Cited By: 0; Correspondence Address: Q. Wang; Department of Applied Mathematics and Process Control, Saint Petersburg State University, Saint Petersburg, Russian Federation; email: wangq506@outlook.com; Conference name: 9th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2025; Conference date: 5 November 2025 through 7 November 2025; Conference code: 344719; null ; Conference date: 05-11-2025 Through 07-11-2025",
year = "2026",
doi = "10.1007/978-3-032-13612-1_14",
language = "Английский",
isbn = "9783032136114",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Nature",
pages = "144--154",
booktitle = "Proceedings of the Ninth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI{\textquoteright}25), Volume 2",
address = "Германия",

}

RIS

TY - GEN

T1 - Multi-task Deep Convolutional Neural Network Based on YOLOv11 for Tomato Fruit Ripening Detection

AU - Wang, Q.

AU - Fu, G.

AU - Li, Z.

N1 - Export Date: 29 March 2026; Cited By: 0; Correspondence Address: Q. Wang; Department of Applied Mathematics and Process Control, Saint Petersburg State University, Saint Petersburg, Russian Federation; email: wangq506@outlook.com; Conference name: 9th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2025; Conference date: 5 November 2025 through 7 November 2025; Conference code: 344719

PY - 2026

Y1 - 2026

N2 - 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.

AB - 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.

KW - Deep convolutional neural network

KW - Swin-Transformer

KW - Tomato ripeness detection

KW - YOLOv11

KW - Error detection

KW - Food safety

KW - Convolutional neural network

KW - Lycopenes

KW - Multi tasks

KW - Network-based

KW - Reducing waste

KW - Swin-transformer

KW - Tomato fruit ripening

KW - Vitamin C

KW - Fruits

UR - https://www.mendeley.com/catalogue/5c4e0d0e-d55c-37ce-9d45-47623299057e/

U2 - 10.1007/978-3-032-13612-1_14

DO - 10.1007/978-3-032-13612-1_14

M3 - статья в сборнике материалов конференции

SN - 9783032136114

T3 - Lecture Notes in Networks and Systems

SP - 144

EP - 154

BT - Proceedings of the Ninth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’25), Volume 2

PB - Springer Nature

Y2 - 5 November 2025 through 7 November 2025

ER -

ID: 151441971