机器学习评估埃塞俄比亚玉米用地适宜性：GB+SFS 达 99.41% 准确率
Mathematical Formulation and Comparative Performance Evaluation of Machine Learning Algorithms for Maize Land Suitability Prediction
埃塞俄比亚团队用梯度加特征选择预测玉米种哪块地，准确率 99.41%，比传统土地评估省时省钱，搞农业 ML 的可以看看
arXiv 论文研究用机器学习替代传统方法评估埃塞俄比亚玉米种植用地适宜性，覆盖 Amhara 地区 Burie、Shindi、Tilili 三地。数据来自当地农业办公室和调查队，比较了 KNN、梯度提升、随机森林等分类器，并配合 UFS、RFECV、SFS 三种特征选择技术。GB 与 SFS 组合表现最佳，准确率达 99.41%，精确率和召回率分别为 99.41% 和 99.35%。
Mathematical Formulation and Comparative Performance Evaluation of Machine Learning Algorithms for Maize Land Suitability Prediction
Addressing the food needs of a growing population is one of the major challenges of developing countries like Ethiopia. Various policies aimed at increasing food production have been enacted by governments and development partners. Although efforts in this regard have been made, there is still inadequate emphasis on increasing food production through the use of high technology practices. Precision agriculture using technology has been utilized to increase crop yield. Precision agriculture also relies on identifying land for a crop, which is a key factor that affects crop yield. Traditional methods of assessing land suitability for crop production are time-consuming, costly and unreliable. Against this background, the aim of this study was to assess the potential of machine learning (ML) in improving the speed and accuracy of assessing land suitability for maize, a staple crop in Ethiopia, as compared to traditional methods. The study is investigated in three areas (Burie, Shindi and Tilili) in the Amhara region. Data collection was done through the local agriculture offices and survey teams. The data were coded, scaled and balanced. Various feature extraction techniques, which included Univariate Feature Selection(UFS), Recursive Feature Elimination with Cross-Validation(RFECV) and Sequential Feature Selection(SFS), and various classifiers, which were K-nearest neighbor (KNN), gradient boosting (GB),random forest (RF) with mathematical formulations were employed. The best models were constructed through combining GBOOST and SFS and achieved an accuracy of 99.41% and 99.35% for both precision and recall. Our results showed that using machine learning is a reliable method of assessing land suitability for maize in Ethiopia. Keywords: Maize, Gradient boosting,Land suitability,Machine learning, Mathematical formulations