Mequanenit Azanu Mirolgn, Ayalew Aleka Melese, Salau Ayodeji Olalekan, Nibret Eyerusalem Alebachew, Meshesha Million
Department of Information Technology, University of Gondar, Gondar, Ethiopia.
Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria.
Heliyon. 2024 Dec 5;10(24):e40971. doi: 10.1016/j.heliyon.2024.e40971. eCollection 2024 Dec 30.
Agriculture is the backbone of the Ethiopian economy. It plays a significant role in the growth of the national economy. Among these, mung bean production takes the highest level of income for most smallholder farmers in all regions of Ethiopia who cultivate mung bean crops. Therefore, this study aims to predict mung bean production using a machine learning algorithm. For this study, the datasets were collected from the Central Statistical Agency of Ethiopia database. A total of 10273 instances were used for the experiment. Python machine learning tool was used to conduct the experiment and build an optimal model. To achieve the objective of this study, different experiments were conducted using Random Forest, Gradient Boosting, and Xgboosting algorithms. In addition, the predictive performances of the classifiers are evaluated and compared using accuracy, precision, recall, F1-score, and confusion matrix. Experimental result shows that the Xgboosting classifiers algorithm achieves the best performance with 98.65 % test accuracy and 99.8 % train accuracy. As a result, the Xgboosting classifier was selected for implementing the model to predict mung bean production. The findings of this study show that the main determinant factors for mung bean production include Meher season, use of extension program, fertilizer used, and fertilizer type. Therefore, the outcome of this research is essential to support the decision-making process of experts and it will enable them take corrective measures on the factors affecting mung bean production.
农业是埃塞俄比亚经济的支柱。它在国民经济增长中发挥着重要作用。其中，绿豆生产为埃塞俄比亚所有种植绿豆作物地区的大多数小农户带来了最高水平的收入。因此，本研究旨在使用机器学习算法预测绿豆产量。对于本研究，数据集是从埃塞俄比亚中央统计局数据库收集的。总共10273个实例用于实验。使用Python机器学习工具进行实验并构建最优模型。为实现本研究的目标，使用随机森林、梯度提升和Xgboosting算法进行了不同的实验。此外，使用准确率、精确率、召回率、F1分数和混淆矩阵对分类器的预测性能进行评估和比较。实验结果表明，Xgboosting分类器算法实现了最佳性能，测试准确率为98.65%，训练准确率为99.8%。因此，选择Xgboosting分类器来实现预测绿豆产量的模型。本研究的结果表明，绿豆生产的主要决定因素包括梅赫尔季节、推广项目的使用、使用的肥料和肥料类型。因此，本研究结果对于支持专家的决策过程至关重要，它将使他们能够对影响绿豆生产的因素采取纠正措施。
