Utilizing machine learning to optimize agricultural inputs for improved rice production benefits
Tao Liu,
Xiafei Li,
Xinrui Li,
Zhonglin Wang,
Huilai Yin,
Yangming Ma,
Yongheng Luo,
Ruhongji Liu,
Zhixin Li,
Pengxin Deng,
Zhenglan Peng,
Zhiyuan Yang,
Yongjian Sun,
Jun Ma,
Zongkui Chen
Affiliations
Tao Liu
Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China
Xiafei Li
Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China
Xinrui Li
Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China
Zhonglin Wang
Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China
Huilai Yin
Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China
Yangming Ma
Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China
Yongheng Luo
Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China
Ruhongji Liu
Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China
Zhixin Li
Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China
Pengxin Deng
Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China
Zhenglan Peng
Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China
Zhiyuan Yang
Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China
Yongjian Sun
Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China
Jun Ma
Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China
Zongkui Chen
Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China; Corresponding author
Summary: Lower efficiency of agricultural inputs in the four conventional rice planting methods limits productivity and environmental benefits in Southwest China. Thus, we developed a machine-learning-based decision-making system for achieving optimal comprehensive benefits during rice production. Based on conventional benefits for achieving optimal benefits, implemented strategies in these planting methods: reducing N fertilizer by 16% while increasing seed inputs by 9% in mechanical transplanting (MT) method improved yield and environmental benefits; reducing N fertilizer and seed inputs by 10–12% in mechanical direct seeding (MD) method decreased environmental impacts; increasing N-K fertilizers and seed inputs by 15–33% in manual transplanting (MAT) method improved its comprehensive benefits by 7–14%; applying N-P-K fertilizer ratio of 2:1:2 in manual direct seeding (MAD) method enhanced yield. Our study provides strategies for improving benefits in these planting methods, with MT method being more beneficial for optimizing comprehensive benefits, especially in yield and environmental impacts, in Southwest China.
