Using Machine Learning to Forecast the Cultivation Intentions of Idle Farmland Owners: A Case Study of Chiba and Ibaraki Prefectures
Using Machine Learning to Forecast the Cultivation Intentions of Idle Farmland Owners: A Case Study of Chiba and Ibaraki Prefectures
复制标题
利用机器学习预测闲置农田所有者的耕种意图：以千叶县和茨城县为例
DOI：
10.11472/nokei.94.191
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
丸山 敦史
中科院分区：
文献类型：
--
作者：
栗原 伸一;丸山 敦史
当农业政策制定者推动闲置农田向主导农户积累时，高精度预测土地所有者的意图（即出售、出租或耕种的意图）比阐明结构性因素更为重要。在本研究中，我们试图通过指定基于东京都市区两个区域的农业土地信息系统的支持向量机（SVM）来解决这个问题。结果，SVM在训练数据集和测试数据集上表现出较高的预测精度。
When agricultural policymakers promote the accumulation of idle farmland to leading farmers, it is more important to predict the intentions of landowners (ie, intentions to sell, rent, or cultivate) with high accuracy than to elucidate structural factors. In this study, we attempted to solve this problem by specifying a support vector machine (SVM) based on agricultural land information systems in two regions of the Tokyo metropolitan area. As a result, the SVM showed high prediction accuracy on the training data set and the test data set.