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Verification of improved YOLOX model in detection of greenhouse crop organs: Considering tomato as example

文献类型： 外文期刊

作者： Zhang, Fujie 1 ; Lv, Zhiyuan 1 ; Zhang, Huixin 3 ; Guo, Jia 3 ; Wang, Jian 3 ; Lu, Tiangang 3 ; Zhangzhong, Lili 4 ;

作者机构： 1.Kunming Univ Sci & Technol, Fac Modern Agr Engn, Kunming 650500, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

3.Beijing Digital Agr Promot Ctr, Beijing 100101, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

关键词： Agriculture; Deep learning; Anchor-free; YOLOX net; Real-time detection

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2023 年 205 卷

页码：

收录情况： SCI

摘要： Efficient and accurate monitoring crop growth is an important prerequisite for modern greenhouse crop growth control and management. However, it is still challenging to achieve efficient in coordinated detection of tomato organs. Therefore, in this study, a multiobjective detection model was established to detect the blossom, fruit, and fruit color of crop population under dense planting, leaf shading, fruit overlap, and interference from light change in greenhouses. Specifically, sample equalization, random transformation, and mosaic enhancement were applied to identify multiple targets and small-sample images. A new detection network, YOLOX, was used, which introduces a decoupled head structure and redesigns a detection head according to the anchor-free concept to effectively improve the recognition of small-target occlusion. An efficient tomato flower and fruit recognition model was constructed based on a two-step fine-tuned multi-round optimization strategy training. The results showed that the YOLOX target detection model improved the average recognition accuracy (mAP) by 52.49-10.75 %, was lighter, was only 14 % original YOLOv4, and improved the detection speed by 25.53 FPS compared to six main target detection algorithms. It can be used as a method for the collaborative recognition of tomato flowers and fruits. Accordingly, two sets of lightweight detection networks with different performances were designed according to different agricultural scenarios. YOLOXMOB modeled with a small size (only 34.2 MB), high mAP of 81.39 %, and detection speed of 61.85 FPS. It is recommended for use as a deployment model for mobile devices. YOLOXPC, with a deep network layer and strong performance, achieved 86.16 % mAP, and is recommended for monitoring and dispatching computers. This study puts forward a set of multiobjective detection model scheme adapted to the real environment of greenhouse, which provides ideas for the construction of a highly intelligent monitoring system platform for the agricultural Internet of Things.

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