Agriculture exhibitions an important role in the progression and enlargement of the economy of any country. Prediction of crop yield will be useful for farmers, but it is difficult to predict crop yield because of the climatic factors such as rainfall, soil factors and so on. To tackle these issues, we are implementing a novel algorithm called Lemuria by applying data mining in agriculture especially for crop yield analysis and prediction. This novel algorithm is the hybridization of classifiers for pre-training, training and testing: deep belief network for feature learning, k-means clustering together with particle swarm optimization (PSO) to get the global solution as well as naïve Bayes clustering with PSO for testing. The performance of the Lemuria algorithm is evaluated in Python, which provides an accuracy of 97.74% for crop prediction by considering the rainfall dataset and also stated that this gives the optimum results in comparison with the existing methodologies.

中文翻译：

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Lemuria：一种使用数据挖掘的新型未来作物预测算法

农业展览会在任何国家的经济发展和壮大中都发挥着重要作用。预测作物产量对农民很有用，但由于气候因素（如降雨，土壤因素等），很难预测作物产量。为了解决这些问题，我们通过将数据挖掘应用于农业，尤其是用于农作物产量的分析和预测，正在实施一种称为Lemuria的新颖算法。这种新颖的算法是用于预训练，训练和测试的分类器的混合：用于特征学习的深度置信网络，k-均值聚类与粒子群优化（PSO）一起获得全局解决方案，以及将朴素贝叶斯聚类与PSO进行测试。Lemuria算法的性能在Python中进行了评估，通过考虑降雨数据集，该算法可为农作物预测提供97.74％的准确度，并且指出与现有方法相比，该方法可提供最佳结果。