A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters
A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters
复制标题
DOI：
10.1007/s12652-020-02752-y
复制
发表时间：
2021-01-01
影响因子：
--
通讯作者：
Vincent, P. M. Durai Raj
中科院分区：
文献类型：
--
作者：
Elavarasan, Dhivya;Vincent, P. M. Durai Raj
技术和科学的发展有助于将各个农业领域的大量数据汇集到公共领域。利用气候、土壤和水分参数预测作物产量是一个很有潜力的研究课题.因此，一个目标是将数据与农业过程相结合，以提高作物产量。本文提出了一种新的基于混合回归的算法，即强化随机森林，它比传统的机器学习技术（如随机森林，决策树，梯度提升，人工神经网络和深度Q学习）显示出显着增强的性能。新策略在树构建过程中每次选择分裂属性时执行强化学习，以有效利用可用样本。他们分析变量的重要性度量，以选择最重要的变量节点分裂过程中的模型开发，并促进训练数据的有效利用。这种集成的混合程序提供了显着的增强，特别是稀疏模型结构的流行策略。除了执行内部交叉验证，所提出的方法需要更少的参数调整，减少过拟合，更快的计算和更透明。用均方根误差、均方误差、决定系数和平均绝对误差等多种评价指标对实验模型进行评价。结果表明，该方法具有更好的性能，减少了误差措施，提高了92.2%的准确性。
The development in technology and science has contributed to a vast volume of data from various agrarian fields to be aggregated in the public domain. Predicting the crop yield based on climate, soil and water parameters has been a potential re- search subject. Therefore an objective arises in integrating the data with agrarian processes for crop enhancement. In this paper, a new hybrid regression-based algorithm, Reinforcement Random Forest is proposed which displays significantly enhanced performance over traditional machine learning techniques like the random forest, decision tree, gradient boosting, artificial neural network and deep Q-learning. The new strategy executes reinforcement learning at every selection of a splitting attribute amid the process of tree construction for the efficient utilization of the available samples. They analyze the variable significance measure to select the most substantial variable for node splitting process in the model development and promotes efficient utilization of training data. This integrated hybrid procedure provides significant enhancement over prevailing strategies specifically for sparse model structures. Besides performing internal cross-validation, the proposed approach requires less parameter tuning, reduces over-fitting, faster calculation and more transparent. The experimented models are evaluated with various assessment metrics like Root Mean Squared Error, Mean Squared Error, Determination Coefficient and Mean Absolute Error. The results obtained delineated that the proposed approach performs better with reduced error measures and improved accuracy of 92.2%.