Dependency analysis of various factors and ML models related to Fertilizer Recommendation

S. Vaishnavi, Nandikaa Shanmugam, Galla Kiran, A. Priyadharshini
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Abstract

Adopting the same fertilizer gives minimum yield to the farmers as soil properties have changed drastically due to the change in environmental condition. In literature, different algorithmic analysis has been carried out to predict the fertilizer considering various factors, however, there is a gap in identifying every possible factor relevant to fertilizer recommendation. Hence, in our proposed work, we have utilized various soil and environmental factors like Nitrogen, Phosphorus and Potassium values, humidity, rainfall, weather condition and performed a dependency analysis of these factors to give a more accurate fertilizer prediction so as to enhance the crop yield. Algorithms such as Random Forest, Decision Tree, Support Vector Machine (SVM), Naïve Bayes (NB) and Logistic Regression (LR) have been explored to study the suitability of these algorithms in fertilizer prediction. The presented algorithms are compared based on the performance metrics such as accuracy, F1 score, Recall and precision. It is found that, among other algorithms, SVM performed better with maximum accuracy of 97% when all the factors are taken into account.
肥料推荐相关因素及ML模型的相关性分析
由于环境条件的变化,土壤性质发生了巨大的变化,因此采用相同的肥料只能使农民获得最低的产量。在文献中,已经进行了不同的算法分析来考虑各种因素来预测肥料,但是在识别每个可能与肥料推荐相关的因素方面存在差距。因此,在我们提出的工作中,我们利用氮、磷、钾值、湿度、降雨量、天气条件等各种土壤和环境因子,并对这些因子进行相关性分析,以更准确地预测肥料,从而提高作物产量。研究了随机森林(Random Forest)、决策树(Decision Tree)、支持向量机(Support Vector Machine, SVM)、Naïve贝叶斯(Bayes, NB)和逻辑回归(Logistic Regression, LR)等算法在肥料预测中的适用性。基于准确率、F1分数、召回率和精度等性能指标对提出的算法进行了比较。研究发现,在其他算法中,当考虑到所有因素时,SVM的准确率最高可达97%。
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