After wheat, rice is one of the most important agricultural products in the world, and Iran has a special position here with annual production of more than 2 million t of rice. Evaluation of crop yield has an important role in agricultural policy making due to different conditions and restrictions. Estimating rice yield is a key factor in food security. Any change in the effective parameters can cause changes in rice yield and therefore the food security of the population will be affected. In this study, rice crop yield was estimated by artificial neural networks (ANNs) and ANN-genetic programming (GP) in 2011 and 2015. Rainfall, permeability, soil texture, land type, evapotranspiration and inlet and inflow and outflow water to paddy lands were used as inputs. The results showed that the ANN-GP with a root mean square error (RMSE = 80.8 kg ha‾¹) and a correlation coefficient (CC = 0.91) was more accurate than the stand-alone ANN (with RMSE = 139 kg ha‾¹ and CC = 0.67). Finally, the effect of each input parameter on rice yield was evaluated. Irrigation, drainage and soil type parameters had the best impact rank, with 36, 28 and 31%, respectively. Therefore, the proposed method can act as an efficient tool in estimating rice yield and help decision makers to manage and develop the agricultural system.

中文翻译：

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估算水稻产量的人工智能方法*

大米是继小麦之后世界上最重要的农产品之一，伊朗在此具有特殊地位，年产大米超过200万吨。由于不同的条件和限制，作物产量评估在农业政策制定中具有重要作用。估算水稻产量是粮食安全的关键因素。有效参数的任何变化都会导致水稻产量的变化，从而影响人口的粮食安全。在这项研究中，2011 年和 2015 年，通过人工神经网络 (ANNs) 和 ANN 遗传编程 (GP) 估算了水稻作物产量。 降雨量、渗透性、土壤质地、土地类型、蒸发量以及进入稻田的进出水量被用作输入。结果表明，ANN-GP 的均方根误差 (RMSE = 80. 8 kg ha‾¹) 和相关系数 (CC = 0.91) 比独立 ANN 更准确（RMSE = 139 kg ha‾¹ 和 CC = 0.67）。最后，评估了每个输入参数对水稻产量的影响。灌溉、排水和土壤类型参数的影响等级最高，分别为 36%、28% 和 31%。因此，所提出的方法可以作为估算水稻产量的有效工具，并帮助决策者管理和发展农业系统。