Dynamic regulation of the irrigation-nitrogen-biochar nexus for the synergy of yield,quality,carbon emission and resource use efficiency in tomato

Integrated water and fertilizer management is important for promoting sustainable development of facility agriculture,and biochar plays an important role in guaranteeing food production,as well as alleviating water shortages and the overuse of fertilizers.The field experiment had twelve treatments and a control(CK)trial including two irrigation amounts(I1,100%ETm;I2,60%ETm;where ETm is the maximum evapotranspiration),two nitrogen applications(N1,360 kg ha^(−1);N2,120 kg ha^(−1))and three biochar application levels(B1,60 t ha^(−1);B_(2),30 t ha^(−1)and B3,0 t ha^(−1)).A multi-objective synergistic irrigation-nitrogen-biochar application system for improving tomato yield,quality,water and nitrogen use efficiency,and greenhouse emissions was developed by integrating the techniques of experimentation and optimization.First,a coupled irrigation-nitrogen-biochar plot experiment was arranged.Then,tomato yield and fruit quality parameters were determined experimentally to establish the response relationships between irrigation-nitrogen-biochar dosage and yield,comprehensive quality of tomatoes(TCQ),irrigation water use efficiency(IWUE),partial factor productivity of nitrogen(PFPN),and net greenhouse gas emissions(NGE).Finally,a multi-objective dynamic optimization regulation model of irrigation-nitrogen-biochar resource allocation at different growth stages of tomato was constructed which was solved by the fuzzy programming method.The results showed that the application of irrigation and nitrogen to biochar promoted increase in yield,IWUE and PFPN,while it had an inhibitory effect on NGE.In addition,the optimal allocation amounts of water and fertilizer were different under different scenarios.The yield of the S1 scenario increased by 8.31%compared to the B_(1)I_(1)N_(2) treatment;TCQ of the S2 scenario increased by 5.14%compared to the B_(2)I_(2)N_(1) treatment;IWUE of the S3 scenario increased by 10.01%compared to the B1I2N2 treatment;PFPN of the S4 scenario increased by 9.35%compared to the B_(1)I_(1)N_(2) treatment;and NGE of the S5 scenario decreased by 11.23%compared to the B_(2)I1N1 treatment.The optimization model showed that the coordination of multiple objectives considering yield,TCQ,IWUE,PFPN,and NGE increased on average from 4.44 to 69.02%compared to each treatment when the irrigation-nitrogen-biochar dosage was 205.18 mm,186 kg ha^(−1)and 43.31 t ha^(−1),respectively.This study provides a guiding basis for the sustainable management of water and fertilizer in greenhouse tomato production under drip irrigation fertilization conditions.

Acollaborative target tracking algorithm formultiple UAVs with inferior tracking capabilities

Target tracking is one of the hottest topics in the field of drone research.In this paper,we study the multiple unmanned aerial vehicles(multi-UAV)collaborative target tracking problem.We propose a novel tracking method based on intention estimation and effective cooperation for UAVs with inferior tracking capabilities to track the targets that may have agile,uncertain,and intelligent motion.For three classic target motion modes,we first design a novel trajectory feature extraction method with the least dimension and maximum coverage constraints,and propose an intention estimation mechanism based on the environment and target trajectory features.We propose a novel Voronoi diagram,called MDA-Voronoi,which divides the area with obstacles according to the minimum reachable distance and the minimum steering angle of each UAV.In each MDA-Voronoi region,the maximum reachable region of each UAV is defined,the upper and lower bounds of the trajectory coverage probability are analyzed,and the tracking strategies of the UAVs are designed to effectively reduce the tracking gaps to improve the target sensing time.Then,we use the Nash Q-learning method to design the UAVs’collaborative tracking strategy,considering factors such as collision avoidance,maneuvering constraints,tracking cost,sensing performance,and path overlap.By designing the reward mechanism,the optimal action strategies are obtained as the control input of the UAVs.Finally,simulation analyses are provided to validate our method,and the results demonstrate that the algorithm can improve the collaborative target tracking performance for multiple UAVs with inferior tracking capabilities.