【摘要】 提高农业生产技术效率是推动农业高质量发展的重要内容,但传统的基于前沿面的技术效率评价模型在实际应用中存在模型运算速度慢和灵活性低等问题,难以对大量新增样本的效率进行快速评价。基于此,本研究将基于前沿面的DEA技术效率测算模型与集成学习模型相结合,提出一种农业生产技术效率的评估预测方法,并利用葡萄生产技术效率数据集验证了模型的效果。研究结果显示,Stacking融合模型的准确率和AUC分别达到了94.8%和0.984,均优于其他对比模型,表明基于Stacking集成学习模型具有较高的预测准确性,能够实现更加高效、快速的技术效率评价。更多还原

【Abstract】 Improving the technical efficiency of agricultural production is an important part to promote the high-quality development of agriculture. However, in practical application, there exist some flaws in the traditional technical efficiency evaluation model based on the frontier, such as slow computing speed and low flexibility, which make it difficult to evaluate the efficiency of a large number of new samples. For the above reasons, a method for evaluating and predicting the technical efficiency of agricultural production was proposed, which combined the DEA technical efficiency measurement model based on the frontier with the ensemble learning model, and the grape production technical efficiency dataset was used to verify the effect of the model. Experiments showed that the Stacking fusion model reached the accuracy and AUC of 94.8% and 0.984 respectively, with promising result that surpassed the other comparison models, indicating that the Stacking ensemble learning model had high accuracy, robustness and generalization ability, and can achieve more efficient, fast and stable technical efficiency evaluation.更多还原