Application of New Technology of Intelligent Robot Plant Protection in Ecological Agriculture

Crop diseases, pest infestations, water shortages, weed infestations, and other issues affect the agriculture sector. Due to existing agricultural techniques, these issues result in significant crop loss, economic loss, and severe environmental hazards. Because agriculture is such a dynamic industry...

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Bibliographic Details
Published in:Journal of food quality 2022-04, Vol.2022, p.1-7
Main Authors: An, Zhe, Wang, Chunhang, Raj, Bincy, Eswaran, Sathyapriya, Raffik, R., Debnath, Sandip, Rahin, Saima Ahmed
Format: Article
Language:English
Subjects:
Accuracy
Agricultural industry
Agricultural practices
Agricultural production
Agriculture
Algorithms
Automation
China
Climate change
Crop diseases
Crop losses
Design
Environmental hazards
Food quality
Image retrieval
India
Innovations
Lasers
Localization
Plant protection
Robots
Water shortages
Water-supply
Working conditions
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Summary:Crop diseases, pest infestations, water shortages, weed infestations, and other issues affect the agriculture sector. Due to existing agricultural techniques, these issues result in significant crop loss, economic loss, and severe environmental hazards. Because agriculture is such a dynamic industry, robotics cannot solve all of its difficulties; instead, a single solution to a specific complex problem is supplied. To assist with these issues and provide a better approach globally, a variety of systems have been developed. Plant protection robots are characterized by complexity, constraint, and nonlinearity. In order to improve the accuracy and reliability of plant protection robots in agricultural job path planning, we propose a path planning method for agricultural plant protection robots based on a nonlinear algorithm. The ant colony algorithm was selected to plan the path distance index according to the working environment, and the feasibility of the simulation system was calculated. The results show that the fastest time used by the nonlinear algorithm is 5.3, and the path planning accuracy is up to 97.8%. Compared with the traditional algorithm, the algorithm has higher accuracy, less computing time, and higher computing efficiency.
ISSN:0146-9428
1745-4557
DOI:10.1155/2022/1257015