基于天气变量和种子特性的机器学习分类算法在水稻选种方面的比较

Q3 Agricultural and Biological Sciences
Dhinakaran Sakthipriya, Chandrakumar Thangavel
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引用次数: 0

摘要

在作物季节开始之前,选种对农民来说至关重要。因此,本研究尝试对泰米尔纳德邦马杜赖区的三个主要水稻产区,即 Thirumangalam、Peraiyur 和 Usilampatti 的水稻种子种植预测的各种机器学习(ML)分类技术进行比较。为了更好地预测水稻种子,本研究根据气象数据和种子特征对五种机器学习分类技术进行了比较,即 K-近邻(KNN)、决策树(DT)、奈夫贝叶斯(NB)、支持向量机(SVM)和逻辑回归(LR)。评估算法时使用了各种指标,包括 F1 分数、准确度、精确度和召回率。研究结果表明,KNN(K-近邻)的准确度、精确度、召回率和 F1 分数分别为 0.99、0.94、1.0 和 0.96。 它给出了最佳的水稻选种结果,这可能有助于农业社区获得更高的产量和利润。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of machine learning classification algorithms based on weather variables and seed characteristics for the selection of paddy seed
Selection of seed is very crucial for the farmers before the start of the crop season. In this study therefore, an attempt has been made to compare various machine learning (ML) classification techniques for paddy seed forecast for cultivation in three major paddy producing taluk of Madurai district, Tamil Nadu viz Thirumangalam, Peraiyur, and Usilampatti. Five machine learning classification techniques viz. K-nearest neighbour (KNN), decision tree (DT), naive bayes (NB), support vector machine (SVM), and logistic regression (LR) used in this study were compared based on weather data and seed characteristics for the better predictions of a paddy seed. Various measures were used to evaluate the algorithms, including F1-score, accuracy, precision, and recall. The findings indicated that the KNN (K-Nearest Neighbour) gave a better accuracy, precision, recall, and F1-score values of about 0.99, 0.94, 1.0, and 0.96 correspondingly.  It gave the best result of the paddy seed selection which may be helpful for the farming community in getting higher yield and profit.
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来源期刊
Journal of Agrometeorology
Journal of Agrometeorology 农林科学-农艺学
CiteScore
1.40
自引率
0.00%
发文量
95
审稿时长
>12 weeks
期刊介绍: The Journal of Agrometeorology (ISSN 0972-1665) , is a quarterly publication of Association of Agrometeorologists appearing in March, June, September and December. Since its beginning in 1999 till 2016, it was a half yearly publication appearing in June and December. In addition to regular issues, Association also brings out the special issues of the journal covering selected papers presented in seminar symposia organized by the Association.
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