Apple varieties and growth prediction with time series classification based on deep learning to impact the harvesting decisions
文献类型： 外文期刊
作者： Mhamed, Mustafa 1 ; Zhang, Zhao 1 ; Hua, Wanjia 1 ; Yang, Liling 4 ; Huang, Mengning 5 ; Li, Xu 6 ; Bai, Tiecheng 6 ; Li, Han 1 ; Zhang, Man 1 ;
作者机构： 1.Minist Educ, Key Lab Smart Agr Syst Integrat, Beijing 100083, Peoples R China
2.China Agr Univ, Key Lab Agr Informat Acquisit Technol, Minist Agr & Rural Affairs, Beijing, Peoples R China
3.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China
4.Xinjiang Acad Agr Sci, Res Inst Agr Mechanizat, Urumqi 830091, Peoples R China
5.Beijing Univ Technol, Beijing 100124, Peoples R China
6.Tarim Univ, Coll Informat Engn, Alaer, Xinjiang, Peoples R China
关键词： Vision systems; Orchard apple varieties; Orchard Fuji growth stages; Vision transformer; Custom regularization techniques; Time series analysis
期刊名称：COMPUTERS IN INDUSTRY （ 影响因子：9.1； 五年影响因子：10.4 ）
ISSN： 0166-3615
年卷期： 2025 年 164 卷
页码：
收录情况： SCI
摘要： Apples are among the most popular fruits globally due to their health and nutritional benefits for humans. Artificial intelligence in agriculture has advanced, but vision, which improves machine efficiency, speed, and production, still needs to be improved. Managing apple development from planting to harvest affects productivity, quality, and economics. In this study, by establishing a vision system platform with a range of camera types that conforms with orchard standard specifications for data gathering, this work provides two new apple collections: Orchard Fuji Growth Stages (OFGS) and Orchard Apple Varieties (OAV), with preliminary benchmark assessments. Secondly, this research proposes the orchard apple vision transformer method (POA-VT), incorporating novel regularization techniques (CRT) that assist us in boosting efficiency and optimizing the loss functions. The highest accuracy scores are 91.56 % for OFGS and 94.20 % for OAV. Thirdly, an ablation study will be conducted to demonstrate the importance of CRT to the proposed method. Fourthly, the CRT outperforms the baselines by comparing it with the standard regularization functions. Finally, time series analyses predict the 'Fuji' growth stage, with the outstanding training and validation RMSE being 19.29 and 19.26, respectively. The proposed method offers high efficiency via multiple tasks and improves the automation of apple systems. It is highly flexible in handling various tasks related to apple fruits. Furthermore, it can integrate with real-time systems, such as UAVs and sorting systems. This research benefits the growth of apple's robotic vision, development policies, time-sensitive harvesting schedules, and decision-making.
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