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Adaptive spraying decision system for plant protection unmanned aerial vehicle based on reinforcement learning

International Journal of Agricultural and Biological Engineering 2022年第4期15卷 16-26页

作者： Ziyuan Hao Xinze Li Chao Meng Wei Yang Minzan Li Key Laboratory of Smart Agriculture System Integration Ministry of EducationChina Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture and Rural AffairsChina Agricultural UniversityBeijing 100083China

To solve the problem of lacking scientific guidance in aerial pesticide application,this study introduced an adaptive spraying decision system(ASDS)for Unmanned Aerial Vehicle(UAV)spraying to guide the operators of plant protection UAVs to set reasonable spraying parameters under complicated *** minimum applied volume rate,proper spraying velocity,spraying height,and initial droplet size were recommended by the *** key factor of the decision system is the decision model of reinforcement learning based on the actor-critic neural *** specific,the field experimental data were used to train the critic and actor networks,which made the model adaptive to optimize the output of spraying *** with the conventional spraying parameters,the spraying parameters recommended by the ASDS had a positive impact on wheat *** decision results of the ASDS showed that the spraying volume rate was lower in the blocks with a small leaf area *** addition,the spraying volume rate for the whole parcel was reduced by 14%.After UAV spraying,the uniformity of the droplet deposition in the ASDS parcel was better than that in the conventional ***,the penetrability of the droplets and the control efficacy for the brown wheat mite Petrobia latens(Muller)were similar in the two *** ASDS can recommend the optimal spraying parameters to minimize pesticide application.

关键词： unmanned aerial vehicle spraying parameters decision reinforcement learning droplet deposition

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