A Comprehensive Review of Deep Learning in Computer Vision for Monitoring Apple Tree Growth and Fruit Production DOI Creative Commons
Meng Lv,

Yixiao Xu,

Miao Yu

и другие.

Sensors, Год журнала: 2025, Номер 25(8), С. 2433 - 2433

Опубликована: Апрель 12, 2025

The high nutritional and medicinal value of apples has contributed to their widespread cultivation worldwide. Unfavorable factors in the healthy growth trees extensive orchard work are threatening profitability apples. This study reviewed deep learning combined with computer vision for monitoring apple tree fruit production processes past seven years. Three types models were used real-time target recognition tasks: detection including You Only Look Once (YOLO) faster region-based convolutional network (Faster R-CNN); classification Alex (AlexNet) residual (ResNet); segmentation (SegNet), mask regional neural (Mask R-CNN). These have been successfully applied detect pests diseases (located on leaves, fruits, trunks), organ (including blossoms, branches), yield, post-harvest defects. introduced methods, outlined current research these methods production. advantages disadvantages discussed, difficulties faced future trends summarized. It is believed that this important construction smart orchards.

Язык: Английский

A Comprehensive Review of Deep Learning in Computer Vision for Monitoring Apple Tree Growth and Fruit Production DOI Creative Commons
Meng Lv,

Yixiao Xu,

Miao Yu

и другие.

Sensors, Год журнала: 2025, Номер 25(8), С. 2433 - 2433

Опубликована: Апрель 12, 2025

The high nutritional and medicinal value of apples has contributed to their widespread cultivation worldwide. Unfavorable factors in the healthy growth trees extensive orchard work are threatening profitability apples. This study reviewed deep learning combined with computer vision for monitoring apple tree fruit production processes past seven years. Three types models were used real-time target recognition tasks: detection including You Only Look Once (YOLO) faster region-based convolutional network (Faster R-CNN); classification Alex (AlexNet) residual (ResNet); segmentation (SegNet), mask regional neural (Mask R-CNN). These have been successfully applied detect pests diseases (located on leaves, fruits, trunks), organ (including blossoms, branches), yield, post-harvest defects. introduced methods, outlined current research these methods production. advantages disadvantages discussed, difficulties faced future trends summarized. It is believed that this important construction smart orchards.

Язык: Английский

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