.png){kind=icon}
返回
Machine learning for optimized crop selection: a data-driven approach considering climate and soil type data
.png){kind=icon}
PRE
AI
DOI：10.1007/s41060-025-00939-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Agriculture is vital for food security and economic development and optimizing crop selection based on data is key for sustainable farming. This study uses machine learning (ML) models to recommend suitable crops based on climate and soil data in Brazil, addressing a gap in the literature that often focuses on Kaggle data, localized data and individual soil components. Using data from the Brazilian Ministry of Agriculture and the National Institute of Meteorology (INMET), the study aggregates crop productivity data from 32 soil types and 95 crop types farmed in 3961 cities in Brazil with daily climate data. Various ML techniques were explored, including Gradient Boosting, Logistic Regression with Bagging, support vector machine (SVM) with Bagging, and Random Forest, all the ML methods tested different preprocessing methods. Synthetic Minority Over-sampling Technique (SMOTE) addressed class imbalance, and multithreading improved computational efficiency. Results showed that the Random Forest performed best with climate data alone (F1-score of 0.20), while adding soil types increased the score to 0.42. For city-specific data, Gradient Boosting achieved an F1-score of 0.70. Tailored models for specific cities outperformed generalized ones, highlighting the challenges of creating general models. Localized models demonstrated to be easier to train but have less generalizing power.
Keyword:
Machine learning
Agricultural optimization
Recommendation systems
Ensemble learning
期刊
I
IF:
2.8
论文数:
1.1K
被引数:
1.3K
机构
引用论文
Methods for class-imbalanced learning with support vector machines: a review and an empirical evaluation支持向量机处理类别不平衡学习的方法：综述与实证评估
OAAI
OAAI分享收藏分享收藏Nithya, V.; Josephine, M.S.; Jeyabalaraja, V. IoT-based crop yield prediction system in Indian sub-continent using machine learning techniques. Remote Sens. Earth Syst. Sci. 2023, 6, 156–166. [Google Scholar] [CrossRef]Nithya, V.; Josephine, M.S.; Jeyabalaraja, V. 基于物联网和机器学习技术的印度次大陆作物产量预测系统。Remote Sens. Earth Syst. Sci. 2023, 6, 156–166. [Google Scholar] [CrossRef].png)
IF0
PREAI
分享收藏Improving logistic regression on the imbalanced data by a novel penalized log-likelihood function
OAAI
OAAI分享收藏Computational intelligence modeling of hyoscine drug solubility and solvent density in supercritical processing: gradient boosting, extra trees, and random forest models超临界加工中hyoscine药物溶解度和溶剂密度的计算智能建模: 梯度提升，额外的树和随机森林模型SCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏Machine learning-based optimal crop selection system in smart agricultureSCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏Diagnosing Cardiovascular Diseases using Optimized Machine Learning Algorithms with GridSearchCV使用GridSearchCV优化机器学习算法诊断心血管疾病OAAI
OAAI分享收藏A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects集成学习综述: 概念、算法、应用与展望IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏分享收藏
查看更多内容