利用计算机视觉技术对农业区域作物产量进行建模

IF 0.5 Q3 AREA STUDIES
M. Arkhipova
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引用次数: 0

摘要

本文研究了俄罗斯农业地区基于远程获取田间状态信息的作物产量建模的新方法。建议的方法可用于制定指标系统和创建获得更准确估计数所必需的方法平台和模型。与传统的回归模型相比,该方法利用计算机视觉技术收集附加数据。统计假设检验证实了卫星农田照片对提高作物产量预测模型准确性的重要意义。将传统的计量经济工具与各种神经网络进行比较,找出最优模型。所提出的工具使用来自俄罗斯43个地区的100个农田的数据进行了测试,这些农田是根据该地区的作物产量按比例选择的。所进行的分析表明,混合数据神经网络与其他神经(多层感知器和卷积神经网络)和回归模型相比具有优势。在不确定和数据量大的情况下,混合数据神经网络可以帮助获得更准确的估计。此外,虽然环境因素对作物产量有不同的影响,但它们必须与社会经济特征一起加以考虑。使用不同于表信息的新模型和数据类型可以显著提高预测的准确性和解释。分析结果可用于审查和监测区域城市的农业生产,确定农场资源需求,以及为发展农业制定部门性和综合性项目和方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling Crop Yield in Agricultural Regions Using Computer Vision Technology
The article examines new methodologies for modelling crop yield in agricultural regions of Russia based on the use of remote capabilities to get information on the field state. The proposed approach can be applied to develop indicator systems and create methodological platforms and models necessary to obtain more accurate estimates. In comparison with the traditional regression model, this method uses computer vision technology to gather additional data. Statistical hypothesis testing confirmed the significance of satellite photographs of fields for improving the accuracy of crop yield forecasting models. Traditional econometric tools were compared with various neural networks in order to discover the optimal model. The proposed tools were tested using data from 100 agricultural fields located in municipalities of 43 Russian regions, selected in proportion to the volume of crop production in this region. The conducted analysis showed the advantage of the mixed data neural network in comparison with other neural (multilayer perceptron and convolutional neural network) and regression models. In conditions of uncertainty and a large amount of data, the mixed data neural network can help obtain more accurate estimates. Additionally, while environmental factors have different effects on crop yields, they must be considered along with socio-economic characteristics. The use of new models and data types differing from table information can significantly improve the forecasting accuracy and interpretation. The analysis results can be used for examining and monitoring agricultural production in regional municipalities, determining farm resource requirements, as well as for creating sectoral and comprehensive projects and programmes for the development of the agricultural industry.
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来源期刊
CiteScore
1.80
自引率
20.00%
发文量
23
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