Abstract Sustainable and reliable management requires special attention to factors affecting crop yield. In the present study, a hybrid model of genetic algorithm and artificial neural network (GA-ANN) was employed to recognize the importance of nutrients in pistachio yield. One hundred seventy-five points in different pistachio orchards of Rafsanjan and Anar regions, Kerman province, the southeast of Iran, were identified and selected for leaf sampling and yield measurement. The concentration of phosphorus (P), potassium (K), iron (Fe), zinc (Zn), copper (Cu), manganese (Mn), calcium (Ca) and magnesium (Mg) was determined. The hybrid GA-ANN model was implemented in MATLAB software, after statistical analysis and multivariate regression modeling. The results showed that the correlation and linear multiple regression analysis could not justify the variations of pistachio yield in relation to leaves' nutrients concentration. The lowest error of the hybrid GA-ANN model was observed by five features including concentrations of K, Mg, Fe, Zn and Cu. Sensitivity analysis of ANN indicated that the highest relative importance for predicting pistachio yield was related to Cu (34.6%), K (28.2%) and Fe (26.1%). The GA-ANN model was able to solve complex and multi-dimensional problems. The accurate and careful interpretation of the results, obtained from this approach can provide a good insight for optimum farm management planning.

中文翻译：

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使用基于混合 GA-ANN 模型的特征选择研究开心果叶中营养成分与其产量之间的关系

摘要 可持续和可靠的管理需要特别关注影响作物产量的因素。在本研究中，采用遗传算法和人工神经网络 (GA-ANN) 的混合模型来识别营养物质对开心果产量的重要性。在伊朗东南部克尔曼省 Rafsanjan 和 Anar 地区的不同开心果园中确定并选择了 175 个点进行叶片采样和产量测量。测定了磷 (P)、钾 (K)、铁 (Fe)、锌 (Zn)、铜 (Cu)、锰 (Mn)、钙 (Ca) 和镁 (Mg) 的浓度。经过统计分析和多元回归建模后，混合 GA-ANN 模型在 MATLAB 软件中实现。结果表明，相关性和线性多元回归分析不能证明开心果产量与叶片养分浓度相关的变化是合理的。通过五个特征观察到混合 GA-ANN 模型的最低误差，包括 K、Mg、Fe、Zn 和 Cu 的浓度。ANN 的敏感性分析表明，预测开心果产量的最高相对重要性与 Cu (34.6%)、K (28.2%) 和 Fe (26.1%) 相关。GA-ANN 模型能够解决复杂的多维问题。从这种方法获得的结果的准确和仔细解释可以为最佳农场管理计划提供很好的洞察力。通过五个特征观察到混合 GA-ANN 模型的最低误差，包括 K、Mg、Fe、Zn 和 Cu 的浓度。ANN 的敏感性分析表明，预测开心果产量的最高相对重要性与 Cu (34.6%)、K (28.2%) 和 Fe (26.1%) 相关。GA-ANN 模型能够解决复杂的多维问题。从这种方法获得的结果的准确和仔细解释可以为最佳农场管理计划提供很好的洞察力。通过五个特征观察到混合 GA-ANN 模型的最低误差，包括 K、Mg、Fe、Zn 和 Cu 的浓度。ANN 的敏感性分析表明，预测开心果产量的最高相对重要性与 Cu (34.6%)、K (28.2%) 和 Fe (26.1%) 相关。GA-ANN 模型能够解决复杂的多维问题。从这种方法获得的结果的准确和仔细解释可以为最佳农场管理计划提供很好的洞察力。