ABSTRACT

Earlier, crop cultivation was undertaken on the basis of farmers’ hands-on expertise. However, climate change has begun to affect crop yields badly. Consequently, farmers are unable to choose the right crop/s based on soil and environmental factors, and the process of manually predicting the choice of the right crop/s of land has, more often than not, resulted in failure. Accurate crop prediction results in increased crop production. This is where machine learning playing a crucial role in the area of crop prediction. Crop prediction depends on the soil, geographic and climatic attributes. Selecting appropriate attributes for the right crop/s is an intrinsic part of the prediction undertaken by feature selection techniques. In this work, a comparative study of various wrapper feature selection methods are carried out for crop prediction using classification techniques that suggest the suitable crop/s for land. The experimental results show the Recursive Feature Elimination technique with the Adaptive Bagging classifier outperforms the others.

摘要

早些时候，农作物种植是在农民动手的专业知识的基础上进行的。但是，气候变化已开始严重影响作物单产。因此，农民无法根据土壤和环境因素选择合适的农作物，而人工预测合适的土地农作物选择的过程往往导致失败。准确的农作物预测可提高农作物的产量。这是机器学习在作物预测领域中发挥关键作用的地方。作物预报取决于土壤，地理和气候属性。为合适的作物选择合适的属性是特征选择技术所进行的预测的内在部分。在这项工作中，使用分类技术为土地进行预测的分类技术，对各种包装特征选择方法进行了比较研究，以进行作物预测。实验结果表明，采用自适应装袋分类器的递归特征消除技术优于其他方法。