预测气候变化影响下澳大利亚作物产量的深度学习框架

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY
Haydar Demirhan
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引用次数: 0

摘要

准确预测作物产量对确保粮食安全至关重要。在这项研究中,我们开发了一个新的深度神经网络框架来预测澳大利亚的作物产量,同时考虑了气候变化、肥料使用和作物面积的影响。它适用于燕麦、玉米、大米和小麦作物,其预测性能是根据五种统计和机器学习方法进行基准测试的。实现所提议的框架的所有软件代码都是免费提供的。建议的框架对所有考虑的作物类型显示出最高的预测性能。与燕麦、玉米、水稻和小麦的基准方法相比,该方法的平均绝对误差分别降低了23%、38%、39%和40%。与基准方法相比,平均均方根误差降低了19%、25%、37%和29%。然后,它被用来预测在六种不同的气候变化情景下,到2025年澳大利亚所考虑的作物的产量。我们观察到,气候变化虽然对作物产量有一定的促进作用,但满足需求是不可持续的。然而,在减缓气候变化的同时保持作物产量的增长是可能的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A deep learning framework for prediction of crop yield in Australia under the impact of climate change
Accurate prediction of crop yields is essential to ensure food security. In this study, a new deep neural networks framework is developed to predict crop yields in Australia, considering the impact of climate change, fertilizer use, and crop area. It is implemented for oats, corn, rice, and wheat crops, and its forecasting performance is benchmarked against five statistical and machine learning methods. All the software codes for the implementation of the proposed framework are freely available. The proposed framework shows the highest forecasting performance for all the considered crop types. It provides 23%, 38%, 39%, and 40% lower average mean absolute error than the benchmark methods for oat, corn, rice, and wheat crops, respectively. The reductions in average root mean squared error are 19%, 25%, 37%, and 29% over the benchmark methods. Then, it is used to predict yields of the considered crops in Australia towards 2025 under six different climate change scenarios. It is observed that although climate change has some boosting impact on crop yield, it is not sustainable to meet the demand. However, it is possible to keep crop yields rising while mitigating climate change.
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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