The development in science and technical intelligence has incited to represent an extensive amount ofdata from various fields of agriculture. Therefore an objective rises up for the examination of the available data and integrating with processes like crop enhancement, yield prediction, examinationof plant infections etc. Machine learning has up surged with tremendous processing techniques to perceive new contingencies in the multi-disciplinary agrarian advancements. In this pa- per a novel hybrid regression algorithm, reinforced extreme gradient boosting is proposed which displays essentially improved execution over traditional machine learning algorithms like artificial neural networks, deep Q-Network, gradient boosting, ran- dom forest and decision tree. Extreme gradient boosting constructs new models, which are essentially, decision trees learning from the mistakes of their predecessors by optimizing the gradient descent loss function. The proposed hybrid model performs reinforcement learning at every node during the node splitting process of the decision tree construction. This leads to effective utilizationofthesamplesbyselectingtheappropriatesplitattributeforenhancedperformance. Model’sperformanceisevaluated by means of Mean Square Error, Root Mean Square Error, Mean Absolute Error, and Coefficient of Determination. To assure a fair assessment of the results, the model assessment is performed on both training and test dataset. The regression diagnostic plots from residuals and the results obtained evidently delineates the fact that proposed hybrid approach performs better with reduced error measure and improved accuracy of 94.15% over the other machine learning algorithms. Also the performance of probability density function for the proposed model delineates that, it can preserve the actual distributional characteristics of the original crop yield data more approximately when compared to the other experimented machine learning models.

中文翻译：

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增强的XGBoost机器学习模型，用于可持续的智能农业应用

科学技术情报的发展已被认为代表了来自农业各个领域的大量数据。因此，提出了一个目标，以检查可用数据并与诸如作物增产，产量预测，植物感染检查等过程集成。机器学习已经以巨大的处理技术激增，以感知多学科农业发展中的新情况。在本文中，提出了一种新的混合回归算法，该算法提出了增强的极端梯度提升，与传统的机器学习算法（如人工神经网络，深度Q网络，梯度提升，随机森林和决策树）相比，该算法显示出了显着改善的执行力。极端梯度增强可构建新模型，从本质上讲，决策树通过优化梯度下降损失函数从其前辈的错误中学习。提出的混合模型在决策树构造的节点拆分过程中的每个节点上执行强化学习。通过选择适当的拆分属性以增强性能，可以有效利用样本。通过均方误差，均方根误差，绝对绝对误差和确定系数对模型的性能进行评估。为了确保公平评估结果，对训练和测试数据集都进行了模型评估。残差的回归诊断图和获得的结果清楚地说明了以下事实，即所提出的混合方法在减少误差测量和提高准确度94的情况下表现更好。比其他机器学习算法高15％。此外，针对所提出模型的概率密度函数的性能还表明，与其他实验机器学习模型相比，它可以更近似地保留原始农作物产量数据的实际分布特征。