Utilizing machine learning to optimize agricultural inputs for improved rice production benefits.

ABSTRACT: 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

SUBMITTER: Liu T 

PROVIDER: S-EPMC11648248 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature