一种用于估算农业用水效率和产量的混合机器学习方法。
A hybrid machine learning approach for estimating the water-use efficiency and yield in agriculture.
机构信息
Agricultural Research, Education and Extension Organization, Agricultural Engineering Research Institute, Post Box 31585-845, Karaj, Alborz, Iran.
Department of Computer Engineering, University of Bonab, Bonab, Iran.
出版信息
Sci Rep. 2022 Apr 25;12(1):6728. doi: 10.1038/s41598-022-10844-2.
This paper introduces the narrow strip irrigation (NSI) method and aims to estimate water-use efficiency (WUE) and yield in apple orchards under NSI in the Miandoab region located southeast of Lake Urmia using a machine learning approach. To perform the estimation, a hybrid method based on an adaptive neuro-fuzzy inference system (ANFIS) and seasons optimization (SO) algorithm was proposed. According to the irrigation and climate factors, six different models have been proposed to combine the parameters in the SO-ANFIS. The proposed method is evaluated on a test data set that contains information about apple orchards in Miandoab city from 2019 to 2021. The NSI model was compared with two popular irrigation methods including two-sided furrow irrigation (TSFI) and basin irrigation (BI) on benchmark scenarios. The results justified that the NSI model increased WUE by 1.90 kg/m and 3.13 kg/m, and yield by 8.57% and 14.30% compared to TSFI and BI methods, respectively. The experimental results show that the proposed SO-ANFIS has achieved the performance of 0.989 and 0.988 in terms of R criterion in estimating WUE and yield of NSI irrigation method, respectively. The results confirmed that the SO-ANFIS outperformed the counterpart methods in terms of performance measures.
本文介绍了窄行灌溉（NSI）方法，并旨在利用机器学习方法估计位于乌尔米亚湖东南的米安德地区 NSI 下的苹果园的水分利用效率（WUE）和产量。为了进行估计，提出了一种基于自适应神经模糊推理系统（ANFIS）和季节优化（SO）算法的混合方法。根据灌溉和气候因素，提出了六个不同的模型，将 SO-ANFIS 中的参数结合起来。该方法在包含 2019 年至 2021 年米安德市苹果园信息的测试数据集上进行了评估。在基准场景下，将 NSI 模型与两种流行的灌溉方法（双侧沟灌（TSFI）和盆灌（BI））进行了比较。结果表明，与 TSFI 和 BI 方法相比，NSI 模型分别将 WUE 提高了 1.90kg/m 和 3.13kg/m，将产量提高了 8.57%和 14.30%。实验结果表明，SO-ANFIS 在估计 NSI 灌溉方法的 WUE 和产量方面的 R 准则性能分别达到了 0.989 和 0.988。结果证实，SO-ANFIS 在性能指标方面优于对照方法。
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