Agriculture plays a significant role in any country's wealth creation, fulfilling food security needs and employment generation thereby contributing immensely to the country's growth and GDP. However, certain characteristics such as climate and environmental changes have extensively become menacing factors in the agriculture output. As soil is an influential specification influencing the prediction of crop yield, soil analysis becomes imperative and can assist farmers in preliminary adaptations towards better crop planning thereby facilitating higher yields. Machine learning algorithms have materialized in soil fertility prediction as an encouraging method for enhancing production. However, the spread and usage of this method are still limited by the lack of clear applicability due to the uncertainties involved and therefore resulting in false predictions by farmers. In this work, we take into account and address both the problems by incorporating uncertainty quantification utilizing the fishers ratio preprocessing model and Kullback divergent chi-square feature selection for soil fertility prediction. Next, Gustafson-Kessel probabilistic neural network classification uses the soil fertility predictive model to produce the probability distribution as output and the different types of soil fertility level instead of a single value. We analyze and prove that the new method not only provides uncertainty quantification but also minimizes the processing time and false positive rate with high accuracy than the existing soil fertility prediction methods.

中文翻译：

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基于 Kullback 卡方和 Gustafson Kessel 概率神经网络的土壤肥力预测

农业在任何国家的财富创造、满足粮食安全需求和创造就业方面都发挥着重要作用，从而为该国的增长和 GDP 做出了巨大贡献。然而，气候和环境变化等某些特征已广泛成为农业产出的威胁因素。由于土壤是影响作物产量预测的一个有影响力的规范，土壤分析变得势在必行，可以帮助农民初步适应更好的作物规划，从而促进更高的产量。机器学习算法已在土壤肥力预测中成为一种令人鼓舞的提高产量的方法。然而，由于所涉及的不确定性，这种方法的传播和使用仍然受到缺乏明确适用性的限制，从而导致农民的错误预测。在这项工作中，我们通过使用费希尔比率预处理模型和 Kullback 发散卡方特征选择结合不确定性量化来考虑和解决这两个问题，用于土壤肥力预测。接下来，Gustafson-Kessel 概率神经网络分类使用土壤肥力预测模型产生概率分布作为输出和不同类型的土壤肥力水平而不是单一值。