Chongqing Academy of Agricultural Sciences, Chongqing 401329, China.
Sensors (Basel). 2023 Jul 26;23(15):6701. doi: 10.3390/s23156701.
The online automated maturity grading and counting of tomato fruits has a certain promoting effect on digital supervision of fruit growth status and unmanned precision operations during the planting process. The traditional grading and counting of tomato fruit maturity is mostly done manually, which is time-consuming and laborious work, and its precision depends on the accuracy of human eye observation. The combination of artificial intelligence and machine vision has to some extent solved this problem. In this work, firstly, a digital camera is used to obtain tomato fruit image datasets, taking into account factors such as occlusion and external light interference. Secondly, based on the tomato maturity grading task requirements, the MHSA attention mechanism is adopted to improve YOLOv8's backbone to enhance the network's ability to extract diverse features. The Precision, Recall, F1-score, and mAP50 of the tomato fruit maturity grading model constructed based on MHSA-YOLOv8 were 0.806, 0.807, 0.806, and 0.864, respectively, which improved the performance of the model with a slight increase in model size. Finally, thanks to the excellent performance of MHSA-YOLOv8, the Precision, Recall, F1-score, and mAP50 of the constructed counting models were 0.990, 0.960, 0.975, and 0.916, respectively. The tomato maturity grading and counting model constructed in this study is not only suitable for online detection but also for offline detection, which greatly helps to improve the harvesting and grading efficiency of tomato growers. The main innovations of this study are summarized as follows: (1) a tomato maturity grading and counting dataset collected from actual production scenarios was constructed; (2) considering the complexity of the environment, this study proposes a new object detection method, MHSA-YOLOv8, and constructs tomato maturity grading models and counting models, respectively; (3) the models constructed in this study are not only suitable for online grading and counting but also for offline grading and counting.
番茄果实的在线自动成熟度分级和计数对数字化监测果实生长状态和种植过程中的无人精确操作具有一定的促进作用。传统的番茄果实成熟度分级和计数大多是手动完成的，既费时又费力，而且其精度取决于人眼观察的准确性。人工智能和机器视觉的结合在一定程度上解决了这个问题。在这项工作中，首先，使用数码相机获取番茄果实图像数据集，同时考虑到遮挡和外部光干扰等因素。其次，根据番茄成熟度分级任务的要求，采用 MHSA 注意力机制改进 YOLOv8 的骨干，增强网络提取多样化特征的能力。基于 MHSA-YOLOv8 构建的番茄果实成熟度分级模型的精度、召回率、F1 分数和 mAP50 分别为 0.806、0.807、0.806 和 0.864，在略微增加模型大小的情况下提高了模型的性能。最后，由于 MHSA-YOLOv8 的出色表现，构建的计数模型的精度、召回率、F1 分数和 mAP50 分别为 0.990、0.960、0.975 和 0.916。本研究构建的番茄成熟度分级和计数模型不仅适用于在线检测，也适用于离线检测，这极大地有助于提高番茄种植者的收获和分级效率。本研究的主要创新点总结如下：（1）构建了一个来自实际生产场景的番茄成熟度分级和计数数据集；（2）考虑到环境的复杂性，本研究提出了一种新的目标检测方法 MHSA-YOLOv8，并分别构建了番茄成熟度分级模型和计数模型；（3）本研究构建的模型不仅适用于在线分级和计数，也适用于离线分级和计数。
