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A study on phenotypic micro-variation of stored melon based on weight loss rate

文献类型： 外文期刊

作者： Qian, Chunyang 1 ; Sun, Shuguang 1 ; Dong, Chenghu 3 ; Chen, Cunkun 3 ; Liu, Wei 2 ; Du, Taihang 1 ;

作者机构： 1.Hebei Univ Technol, Sch Artificial Intelligence, Tianjin 300130, Peoples R China

2.Tianjin Acad Agr Sci, Tianjin 300192, Peoples R China

3.Minist Agr & Rural Affairs, Natl Engn Technol Res Ctr Preservat Agr Prod, Key Lab Storage Agr Prod, Tianjin Key Lab Postharvest Physiol & Storage Agr, Tianjin 300384, Peoples R China

关键词： Melon; Postharvest storage; Deep learning; Visualization analysis

期刊名称：POSTHARVEST BIOLOGY AND TECHNOLOGY （ 影响因子：7.0； 五年影响因子：6.9 ）

ISSN： 0925-5214

年卷期： 2023 年 204 卷

页码：

收录情况： SCI

摘要： This study proposes a novel deep learning algorithm that uses weight loss rate as a basis for classification and incorporates phenotypic micro-variation features for the dynamic prediction and analysis of melon storage processes. The Resnet50 backbone network was used as a basis to construct deformable residual blocks for nonrigid objects with high similarity over time, allowing the convolutional kernel shape to adapt to the melon surface shape. A hybrid spatial and channel attention mechanism was used to focus more attentional resources on the target regions requiring precise attention. The experimental results indicate that the proposed method outperforms traditional computer vision and machine learning methods in terms of effectiveness, performance, and generalization ability. We evaluate our method on a large dataset of melon fruit images and show that it outperforms existing methods in accurately predicting storage status and optimizing control strategies in time. The proposed method will serve as a powerful tool for other nondestructive detection and accurate prediction of the shelf life of large fruit.

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