Fruit freshness detection based on multi-task convolutional neural network.

Yinsheng Zhang, Xudong Yang, Yongbo Cheng, Xiaojun Wu, Xiulan Sun, Ruiqi Hou, Haiyan Wang

Abstract read

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Article in Current research in food science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed

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2 citing papers in PubMed.

DualStream-RTNet: A Multimodal Deep Learning Framework for Grape Cultivar Classification and Soluble Solid Content Prediction.Foods (Basel, Switzerland) · 2026
Article
Multi-Task NoisyViT for Enhanced Fruit and Vegetable Freshness Detection and Type Classification.Sensors (Basel, Switzerland) · 2025
Article

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7 authors.

Yinsheng ZhangZhejiang Food and Drug Quality & Safety Engineering Research Institute, Zhejiang Gongshang University, Hangzhou, 310018, China.

Xudong YangSchool of Management and E-Business, Zhejiang Gongshang University, Hangzhou, 310018, China.

Yongbo ChengSchool of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing, 210023, China.

Xiaojun WuInstitute of Science and Technology, Jiangnan University, Wuxi, 214122, China.

Xiulan SunSchool of Food Science and Technology, Jiangnan University, Wuxi, 214122, China.

Ruiqi HouZhejiang Food and Drug Quality & Safety Engineering Research Institute, Zhejiang Gongshang University, Hangzhou, 310018, China.

Haiyan WangZhejiang Food and Drug Quality & Safety Engineering Research Institute, Zhejiang Gongshang University, Hangzhou, 310018, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Fruit freshness detection by computer vision is essential for many agricultural applications, e.g., automatic harvesting and supply chain monitoring. This paper proposes to use the multi-task learning (MTL) paradigm to build a deep convolutional neural work for fruit freshness detection. Results: We design an MTL model that optimizes the freshness detection (T Conclusion: This study proves that MTL exploits the mutual correlation between two or more relevant tasks and can maximally share their underlying feature extraction process. we envision this approach to be extended to other domains that involve multiple interconnected tasks.

Indexed as

Convolutional neural networkDepthwise separable convolutionFruit freshnessMulti-task learning

Identifiers

PMID38655189

DOI10.1016/j.crfs.2024.100733

PMCPMC11035072

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LicenceCC BY-NC-ND

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