College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China.
Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Tarim University, Alar 843300, China.
Sensors (Basel). 2024 Oct 18;24(20):6700. doi: 10.3390/s24206700.
At present, tomato transplanting in facility agriculture is mainly manual operation. In an attempt to resolve the problems of high labor intensity and low efficiency of manual operation, this paper designs a clip stem automatic transplanting and seedling picking device based on the yolov5 algorithm. First of all, through the study of the characteristics of tomato seedlings of different seedling ages, the age of tomato seedlings suitable for transplanting was obtained. Secondly, the improved yolov5 algorithm was used to determine the position and shape of tomato seedlings. By adding a lightweight upsampling operator (CARAFE) and an improved loss function, the feature extraction ability and detection speed of tomato seedling stems were improved. The accuracy of the improved yolov5 algorithm reached 92.6%, and mAP_0.5 reached 95.4%. Finally, the seedling verification test was carried out with tomato seedlings of about 40 days old. The test results show that the damage rate of the device is 7.2%, and the success rate is not less than 90.3%. This study can provide a reference for research into automatic transplanting machines.
目前，设施农业中的番茄移栽主要依靠人工操作。为了解决人工操作劳动强度大、效率低的问题，本文基于 yolov5 算法设计了一种夹梗式自动移栽和取苗装置。首先，通过研究不同苗龄番茄苗的特点，确定了适合移栽的番茄苗龄。其次，使用改进的 yolov5 算法来确定番茄苗的位置和形状。通过添加轻量级上采样算子（CARAFE）和改进的损失函数，提高了番茄苗茎的特征提取能力和检测速度。改进的 yolov5 算法的准确率达到 92.6%，mAP_0.5 达到 95.4%。最后，用约 40 天龄的番茄苗进行了苗验证试验。试验结果表明，该装置的损伤率为 7.2%，成功率不低于 90.3%。本研究可为自动移栽机的研究提供参考。
