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26 June 2023 Crop planting recommendation algorithm based on ensemble learning
Guangyang Deng, Chen Dong, Jiabo Chen, Ruoxuan Kong, Jiaxing Gao, Chunqiang Li
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Proceedings Volume 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023); 127211D (2023) https://doi.org/10.1117/12.2683430
Event: 2023 2nd International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2023, Chengdu, China
Abstract
In this paper, we propose a crop planting recommendation algorithm based on ensemble learning to recommend the most suitable crops for farmers based on environmental characteristics and soil element content, which can achieve scientific planting and crop yield increase. Firstly, we adjust the scaling ratio of N (nitrogen), P (phosphor) and K (potassium) elements, which play an important role in crop growth, and use KNN (K-Nearest Neighbor), XGBoost and RF (Random Forest) as weak learners, and use GA (Genetic Algorithm) to optimize the important parameters for KNN, XGBoost and RF, and combine these three weak learners to obtain the ensemble learning model through a soft voting mechanism. After training and testing on the Kaggle public dataset, the accuracy of the crop planting recommendation algorithm based on ensemble learning can reach 94.36%.
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Guangyang Deng, Chen Dong, Jiabo Chen, Ruoxuan Kong, Jiaxing Gao, and Chunqiang Li "Crop planting recommendation algorithm based on ensemble learning", Proc. SPIE 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 127211D (26 June 2023); https://doi.org/10.1117/12.2683430
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KEYWORDS
Machine learning
Potassium
Data modeling
Decision trees
Mathematical optimization
Evolutionary algorithms
Agriculture
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