Design of a precise ensemble expert system for crop yield prediction using machine learning analytics

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-08-05 DOI:10.1002/for.3183
Deeksha Tripathi, Saroj K. Biswas
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引用次数: 0

Abstract

Agriculture is facing significant challenges in the development of crop yield forecasts, which are important aspects of decision-making at the international, regional, and local levels. The area of agriculture is attracting growing attention because of increasing the demand for food supplies. To ensure future food supplies, crop yield prediction (CYP) provides the best decision-making to assist farmers in agricultural yield forecasting efficiently. Nevertheless, CYP is a difficult endeavor because of the intricacy of the underlying mechanisms and the effect of numerous factors, including weather patterns, soil characteristics, and crop management techniques. In today's era, ensemble learning (EL) approaches have recently demonstrated significant promise for enhancing the reliability and accuracy of CYP. The success of the EL techniques depends on several facts, including how the base learner models are trained and how these are combined. This study provides important insights into the EL techniques for CYP. This paper proposes an expert system model named precise ensemble expert system for crop yield prediction (PEESCYP) to predict the best crop for agricultural land. The proposed PEESCYP model employs multiple imputation by chained equation (MICE) data imputation technique to treat the missing values of the collected dataset, the isolation forest (IF) technique for outlier detection, the ant colony optimization (ACO) technique to perform feature selection, robust scaling (RS) technique to perform data normalization, and the extra tree (ET) is used for classification to overcome the variance and overfitting problem of the single classifiers. The measurements of the proposed PEESCYP model have been collected by means of accuracy, precision, recall, and F-1 score using a prepared dataset, which is collected from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), and the proposed model is compared with different single-classifier based ML models, EL models, and various existing models available in the literature. The results of this experiment underline that the proposed PEESCYP model outperforms the others.

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利用机器学习分析设计用于作物产量预测的精确集合专家系统
作物产量预测是国际、地区和地方各级决策的重要方面,而农业在发展作物产量预测方面正面临着重大挑战。由于对粮食供应的需求日益增长,农业领域正受到越来越多的关注。为确保未来的粮食供应,农作物产量预测(CYP)提供了最佳决策,可帮助农民有效地进行农业产量预测。然而,作物产量预测是一项艰巨的任务,因为其背后的机制错综复杂,并受到天气模式、土壤特性和作物管理技术等众多因素的影响。在当今时代,集合学习(EL)方法在提高作物产量预测的可靠性和准确性方面大有可为。集合学习技术的成功取决于多个因素,包括如何训练基础学习模型以及如何将这些模型组合在一起。本研究为 CYP 的 EL 技术提供了重要的启示。本文提出了一种专家系统模型,名为作物产量预测精确集合专家系统(PEESCYP),用于预测农田的最佳作物。所提出的 PEESCYP 模型采用链式方程多重估算(MICE)数据估算技术处理所收集数据集的缺失值,采用隔离森林(IF)技术检测离群值,采用蚁群优化(ACO)技术进行特征选择,采用稳健缩放(RS)技术进行数据归一化,并采用额外树(ET)进行分类,以克服单一分类器的方差和过拟合问题。利用从国际半干旱热带作物研究所(ICRISAT)收集的数据集,通过准确度、精确度、召回率和 F-1 分数对所提出的 PEESCYP 模型进行了测量,并将所提出的模型与不同的基于单分类器的 ML 模型、EL 模型以及文献中现有的各种模型进行了比较。实验结果表明,所提出的 PEESCYP 模型优于其他模型。
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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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