Research on the fixed-point pesticide application algorithm for vegetable greenhouse plant protection robot

doi:10.3969/j.issn.1004-1524.20250522

Acta Agriculturae Zhejiangensis ›› 2026, Vol. 38 ›› Issue (7): 1453-1462.DOI: 10.3969/j.issn.1004-1524.20250522

• Biosystems Engineering • Previous Articles Next Articles

Research on the fixed-point pesticide application algorithm for vegetable greenhouse plant protection robot

MENG Chao(), LI Junying^*(), LU Zheng, ZHANG Jinhao, CHEN Jingye

College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China

Received:2025-08-06 Online:2026-07-25 Published:2026-08-20

Abstract

Abstract:

To solve the issue of plant diseases and insect pests in green vegetable cultivation, a fixed-point pesticide application algorithm has been proposed, which is based on machine vision and the Faster R-CNN model. This algorithm encompasses a diseased plant detection method and a robotic arm pesticide application technique. Machine vision and the Faster R-CNN model are employed to identify diseased plants. The monocular imaging principle is then utilized to determine the positions of these plants, and the fixed-point pesticide application algorithm is subsequently used to guide the parallel robotic arm in applying pesticides precisely. To assess the algorithm’s feasibility, both simulation and prototype experiments were conducted. The results indicate that the diseased plant detection algorithm has a maximum single computation time of approximately 43.216 μs, meeting the real-time requirement of the system’s 1 ms communication cycle. At traveling speeds of 100-200 mm·s^-1, the parallel robotic arm’s fixed-point pesticide application algorithm achieved both a miss rate and a false processing rate below 5%. These outcomes confirm the algorithm’s accuracy and stability, providing a technical reference for precision pesticide application in plant protection robots.

Key words: machine vision, fixed-point pesticide application, parallel manipulator, diseased plant detection, monocular imaging

CLC Number:

MENG Chao, LI Junying, LU Zheng, ZHANG Jinhao, CHEN Jingye. Research on the fixed-point pesticide application algorithm for vegetable greenhouse plant protection robot[J]. Acta Agriculturae Zhejiangensis, 2026, 38(7): 1453-1462.

Figures/Tables 17

Fig.1 Implementation of deep reinforcement learning

Fig.2 Faster R-CNN model structure

Table 1 Test parameters

主干网络 Backbone network	准确率/% Precision/%	召回率/% Recall/%	F₁值/% F₁score/%	检测时间/ms Inference time/ms
ResNet50	87.62	89.90	88.75	175.24
ResNet10	88.33	90.85	89.57	174.89
VGG-16	93.89	95.42	94.65	171.11
VGG-19	89.25	89.34	89.29	173.67

Fig.3 Detection examples of healthy and diseased plants by the VGG-16 model

Fig.4 Schematic diagram of visual identification zone and dynamic fixed-point spraying zone

Fig.5 Door-shaped arc-transition spraying path of the robotic arm

Fig.6 Schematic diagram of dynamic targeted spraying

Fig.7 Simulation environment training framework

Fig.8 Modeling interface of parallel spraying robotic arm in CoppeliaSim

Fig.9 Equivalent cubic model of bok choy

Table 2 Hyperparameter settings for DDPG training

参数 Parameter	值 Value
行动者网络学习率Actor network learning rate	0.000 1
评论家网络学习率Critic network learning rate	0.001
训练轮数Number of training episodes	2 000
回放缓冲区空间Replay buffer capacity	80 000
单次更新采样Sampling batch size	200
单次更新子集长度Subsequence length for each update	10
损失系数Discount factor	0.99
衰减系数Soft update coefficient	1×10^-4

Fig.10 Learning curve of the DDPG model

Fig.11 Schematic diagram of DDPG path coordinate projection onto the image plane

Fig.12 Experimental prototype

Fig.13 Computing time test of the disease plant detection algorithm

Fig.14 Dynamic spot application experiment

Table 3 Experimental results of the prototype under different traveling speeds

机器人行进速度/(mm·s^-1) Robot traveling speed/(mm·s^-1)	患病植株总个数 Total number of diseased plants	漏处理个数 Number of missed plants	误处理个数 Number of falsely treated plants
100	24	0	0
150	27	1	1
200	21	1	1

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Research on the fixed-point pesticide application algorithm for vegetable greenhouse plant protection robot

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