模拟农民在采用农业生态实践时的行为,在油农林系统中使用人工智能工具
D S J C Gbemavo1,2, J Laly1,2, V N Adjahossou2
1Biostatistics and Modelling Unit (UBM)/Laboratory of Genetics, Biotechnology and Applied Botany (GEBBA)/ National School of Biosciences and Applied Biotechnology (ENSBBA), National University of Sciences, Technologies, Engineering and Mathematics (UNSTIM Abomey), BP 14 Dassa Zounmé, Dassa Zounmé, Benin.
Heliyon
|December 11, 2024
概括
机器学习模型确定了在羊皮系统中采用农业生态实践的关键因素. 农民 农民 是一个农民.
科学领域:
- 农业科学 农业科学
- 机器学习 机器学习
- 农业林业系统农业林业系统
背景情况:
- 芝麻农林系统对于西非的生计和粮食安全至关重要.
- 采用可持续的农业生态实践对于加强这些系统至关重要.
- 了解采用驱动器对于有效干预至关重要.
研究的目的:
- 评估机器学习算法在识别农业生态实践采用的重要特征方面的性能.
- 为了比较四种机器学习模型:天真贝叶斯,神经网络,支持向量机器和包装决策树.
- 确定影响采用特定实践的关键社会经济和人口因素.
主要方法:
- 数据来自贝宁北部两个地区的272名农民.
- 我们使用了四种机器学习算法,并使用精度,平衡精度,回忆和AUC进行了比较.
- 进行了特征重要性分析,以确定采用的重要预测因素.
主要成果:
- 天真贝叶斯模型在预测动物停车和生物农药采用方面表现出色.
- 支持向量机器对于文化协会,改良种子和有机肥料来说是优越的.
- 农民的原产地区,主要职业,交通工具,族群,村庄,年龄阶级和农场资产被确定为重要特征.
结论:
- 机器学习有效地识别了影响农业生态实践在shea系统中采用的关键因素.
- 建议针对性培训和支持,考虑区域和人口因素,以促进可持续实践.
- 提高采用率可以有助于可持续开发雪资源,并提高粮食安全.
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