您好，欢迎访问新疆农业科学院 机构知识库！
新疆农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Apple varieties, diseases, and distinguishing between fresh and rotten through deep learning approaches

文献类型： 外文期刊

作者： Zhang, Tao 1 ; Mhamed, Mustafa 1 ; Zhang, Qu 2 ; Yang, Liling 5 ; Zhao, Xiaohui 6 ; Haiyan, Gu 7 ; Zhang, Zhao 2 ;

作者机构： 1.Hebei Inst Mech & Elect Technol, Dept Mat & Architectural Engn, Xingtai, Peoples R China

2.Minist Educ, Key Lab Smart Agr Syst Integrat, Beijing, Peoples R China

3.China Agr Univ, Key Lab Agr Informat Acquisit Technol, Minist Agr & Rural Affairs, Beijing, Peoples R China

4.China Agr Univ, Coll Informat & Elect Engn, Beijing, Peoples R China

5.Xinjiang Acad Agr Sci, Res Inst Agr Mechanizat, Urumqi, Peoples R China

6.China Agr Univ, Int Off, Beijing, Peoples R China

7.Shandong Business Inst, Informat Engn Coll, Yantai, Shandong, Peoples R China

期刊名称：PLOS ONE （ 影响因子：2.6； 五年影响因子：3.2 ）

ISSN：

年卷期： 2025 年 20 卷 5 期

页码：

收录情况： SCI

摘要： Apples are one of the most productive fruits in the world, in addition to their nutritional and health advantages for humans. Even with the continuous development of AI in agriculture in general and apples in particular, automated systems continue to encounter challenges identifying rotten fruit and variations within the same apple category, as well as similarity in type, color, and shape of different fruit varieties. These issues, in addition to apple diseases, substantially impact the economy, productivity, and marketing quality. In this paper, we first provide a novel comprehensive collection named Apple Fruit Varieties Collection (AFVC) with 29,750 images through 85 classes. Second, we distinguish fresh and rotten apples with Apple Fruit Quality Categorization (AFQC), which has 2,320 photos. Third, an Apple Diseases Extensive Collection (ADEC), comprised of 2,976 images with seven classes, was offered. Fourth, following the state of the art, we develop an Optimized Apple Orchard Model (OAOM) with a new loss function named measured focal cross-entropy (MFCE), which assists in improving the proposed model's efficiency. The proposed OAOM gives the highest performance for apple varieties identification with AFVC; accuracy was 93.85%. For the apples rotten recognition with AFQC, accuracy was 98.28%. For the identification of the diseases via ADEC, it was 99.66%. OAOM works with high efficiency and outperforms the baselines. The suggested technique boosts apple system automation with numerous duties and outstanding effectiveness. This research benefits the growth of apple's robotic vision, development policies, automatic sorting systems, and decision-making enhancement.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：新疆农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台