Predictive Model of Granular Fertilizer Spreading Deposition Distribution Based on GA-GRNN Neural Network
In this paper, we investigate the particle deposition distribution characteristics in granular fertilizer spreading, establish a relationship model between operational parameters and particle deposition distribution, and design an unmanned aerial vehicle (UAV) fertilizer particle deposition predicti...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2504-446X/9/1/16 |
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| author | Lilian Liu Guobin Wang Yubin Lan Xinyu Xue Suming Ding Huizheng Wang Cancan Song |
| author_facet | Lilian Liu Guobin Wang Yubin Lan Xinyu Xue Suming Ding Huizheng Wang Cancan Song |
| author_sort | Lilian Liu |
| collection | DOAJ |
| description | In this paper, we investigate the particle deposition distribution characteristics in granular fertilizer spreading, establish a relationship model between operational parameters and particle deposition distribution, and design an unmanned aerial vehicle (UAV) fertilizer particle deposition prediction system based on neural network decision making, which provides a decision-making basis for the variable fertilizer application model under multifactorial interactions. The particle deposition distribution data under different operating parameters were obtained by EDEM simulation and data superposition methods, and a generalized regression neural network (GRNN) based on a genetic algorithm (GA) was used to establish the prediction model of particle deposition, which was validated by bench test. The results show that the prediction accuracy and training effect of the GA-GRNN model are better than those of the GRNN, with a coefficient of determination of 0.839, and that the results of the GA-GRNN model are closer to the actual data when predicting the effective amplitude of the deposition amount, which is more accurate. The bench-scale validation test shows that the simulation is basically consistent with the actual measured deposition amount, and the deposition curve is normally distributed with a lateral error of about 3%. The results validate the reliability of the data superposition method for particle deposition distribution and the feasibility of the GA-GRNN model in multifactor prediction, which provides a theoretical basis and practical guidance for precision fertilizer application operations using agricultural UAVs. |
| format | Article |
| id | doaj-art-b12ba7d561e04379acbf4758894c952c |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-b12ba7d561e04379acbf4758894c952c2025-01-24T13:29:39ZengMDPI AGDrones2504-446X2024-12-01911610.3390/drones9010016Predictive Model of Granular Fertilizer Spreading Deposition Distribution Based on GA-GRNN Neural NetworkLilian Liu0Guobin Wang1Yubin Lan2Xinyu Xue3Suming Ding4Huizheng Wang5Cancan Song6College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255049, ChinaCollege of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255049, ChinaCollege of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255049, ChinaNanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Development; Nanjing 210018, ChinaNanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Development; Nanjing 210018, ChinaCollege of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255049, ChinaCollege of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255049, ChinaIn this paper, we investigate the particle deposition distribution characteristics in granular fertilizer spreading, establish a relationship model between operational parameters and particle deposition distribution, and design an unmanned aerial vehicle (UAV) fertilizer particle deposition prediction system based on neural network decision making, which provides a decision-making basis for the variable fertilizer application model under multifactorial interactions. The particle deposition distribution data under different operating parameters were obtained by EDEM simulation and data superposition methods, and a generalized regression neural network (GRNN) based on a genetic algorithm (GA) was used to establish the prediction model of particle deposition, which was validated by bench test. The results show that the prediction accuracy and training effect of the GA-GRNN model are better than those of the GRNN, with a coefficient of determination of 0.839, and that the results of the GA-GRNN model are closer to the actual data when predicting the effective amplitude of the deposition amount, which is more accurate. The bench-scale validation test shows that the simulation is basically consistent with the actual measured deposition amount, and the deposition curve is normally distributed with a lateral error of about 3%. The results validate the reliability of the data superposition method for particle deposition distribution and the feasibility of the GA-GRNN model in multifactor prediction, which provides a theoretical basis and practical guidance for precision fertilizer application operations using agricultural UAVs.https://www.mdpi.com/2504-446X/9/1/16unmanned aerial dispersalGRNNgenetic algorithmdepositional characteristicseffective width |
| spellingShingle | Lilian Liu Guobin Wang Yubin Lan Xinyu Xue Suming Ding Huizheng Wang Cancan Song Predictive Model of Granular Fertilizer Spreading Deposition Distribution Based on GA-GRNN Neural Network Drones unmanned aerial dispersal GRNN genetic algorithm depositional characteristics effective width |
| title | Predictive Model of Granular Fertilizer Spreading Deposition Distribution Based on GA-GRNN Neural Network |
| title_full | Predictive Model of Granular Fertilizer Spreading Deposition Distribution Based on GA-GRNN Neural Network |
| title_fullStr | Predictive Model of Granular Fertilizer Spreading Deposition Distribution Based on GA-GRNN Neural Network |
| title_full_unstemmed | Predictive Model of Granular Fertilizer Spreading Deposition Distribution Based on GA-GRNN Neural Network |
| title_short | Predictive Model of Granular Fertilizer Spreading Deposition Distribution Based on GA-GRNN Neural Network |
| title_sort | predictive model of granular fertilizer spreading deposition distribution based on ga grnn neural network |
| topic | unmanned aerial dispersal GRNN genetic algorithm depositional characteristics effective width |
| url | https://www.mdpi.com/2504-446X/9/1/16 |
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