Study on navigation line extraction algorithm for leaf vegetable ridges based on instance segmentations

doi:10.3969/j.issn.1004-1524.20240167

Acta Agriculturae Zhejiangensis ›› 2025, Vol. 37 ›› Issue (3): 701-711.DOI: 10.3969/j.issn.1004-1524.20240167

• Biosystems Engineering • Previous Articles Next Articles

Study on navigation line extraction algorithm for leaf vegetable ridges based on instance segmentations

ZHENG Hang¹^,²(), FENG Haodong³, XUE Xianglei¹^,², YE Yunxiang¹^,², YU Jianlin¹, YU Guohong¹^,²^,^*()

1. Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
2. Key Laboratory of Agricultural Equipment for Hilly and Mountainous Areas in Southeastern China (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hangzhou 310021, China
3. School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China

Received:2024-02-26 Online:2025-03-25 Published:2025-04-02

Abstract

Abstract:

To improve the mobile navigation accuracy of field management equipment for leafy vegetables under facility ridge cultivation mode, a deep learning based algorithm for extracting navigation lines between leafy vegetable ridges was proposed. Firstly, the model was trained on the navigation path dataset between leafy vegetable ridges. An improved YOLOv5s-Seg convolutional neural network was used to extract feature points from greenhouse operation road images, and the navigation line was generated through least squares fitting. In the experiment, a series of improvements were made to the internal structure of the model, which improved the accuracy and computational speed of the algorithm. By collecting 800 images of the growth of leafy vegetables under ridge planting mode, and expanding the total number of datasets to four times of the original through data augmentation, the overall segmentation average precision was 99.50% on an independent test set composed of 320 images. The precision of images were 94.33%, 97.77% and 96.23% respectively at germination stage, seedling stage, and formation stage. The average deviation between the fitted navigation line and the manually observed navigation line was 5.60 cm. The results showed that the navigation algorithm could meet the navigation requirements of intelligent management of mobile equipment for leafy vegetables in the ridge planting mode within the facility.

Key words: facility greenhouse, ridge planting mode, instance segmentation, deep learning, navigation line

CLC Number:

S225.92

ZHENG Hang, FENG Haodong, XUE Xianglei, YE Yunxiang, YU Jianlin, YU Guohong. Study on navigation line extraction algorithm for leaf vegetable ridges based on instance segmentations[J]. Acta Agriculturae Zhejiangensis, 2025, 37(3): 701-711.

Figures/Tables 16

References 25

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生长时期 Growth stage	准确率 Precision	召回率 Recall	精度均值 Average precision
发芽期	94.33	95.92	97.25
Germination stage
幼苗期	97.77	96.52	98.49
Seedling stage
成形期	96.23	97.73	95.56
Formation stage

生长时期 Growth stage	准确率 Precision	召回率 Recall	精度均值 Average precision
发芽期	94.33	95.92	97.25
Germination stage
幼苗期	97.77	96.52	98.49
Seedling stage
成形期	96.23	97.73	95.56
Formation stage

模型 Model	CA	Ghostconv	Pconv	Mask/%			Parameters	Flops
模型 Model	CA	Ghostconv	Pconv	准确率 Precision	召回率 Recall	精度均值 Average precision	Parameters	Flops
Yolov5s-seg				96.4	99.2	99.1	7 408 214	25.9
Yolov5s-seg+CA	√			98.5	96.9	99.4	7 421 550	25.9
Yolov5s-seg+Ghostconv		√		97.4	98.6	99.3	6 195 894	23.5
Yolov5s-seg+C3_Faster			√	98.1	97.4	99.4	6 177 686	22.8
本文算法Ours	√	√	√	99.6	99.7	99.5	4 978 702	20.4

模型 Model	CA	Ghostconv	Pconv	Mask/%			Parameters	Flops
模型 Model	CA	Ghostconv	Pconv	准确率 Precision	召回率 Recall	精度均值 Average precision	Parameters	Flops
Yolov5s-seg				96.4	99.2	99.1	7 408 214	25.9
Yolov5s-seg+CA	√			98.5	96.9	99.4	7 421 550	25.9
Yolov5s-seg+Ghostconv		√		97.4	98.6	99.3	6 195 894	23.5
Yolov5s-seg+C3_Faster			√	98.1	97.4	99.4	6 177 686	22.8
本文算法Ours	√	√	√	99.6	99.7	99.5	4 978 702	20.4

模型 Model	精度均值 Average precision/%	推理时间 Reasoning time/ms
Faster R-CNN	98.7	185.0
YOLOv7s-Seg	98.6	30.0
YOLOv8s-Seg	99.4	8.1
改进YOLOv5s-Seg	99.5	7.2
Improved YOLOv5s-Seg

Study on navigation line extraction algorithm for leaf vegetable ridges based on instance segmentations

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