| 基于组合色彩特征的苹果树叶片各生长期氮含量预测 |
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| 引用本文： | 王金星,刘雪梅,刘双喜,权泽堃,徐春保,江浩.基于组合色彩特征的苹果树叶片各生长期氮含量预测[J].农业机械学报,2021,52(10):272-281，376. |
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| 作者姓名： | 王金星 刘雪梅 刘双喜 权泽堃 徐春保 江浩 |
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| 作者单位： | 山东农业大学机械与电子工程学院,泰安271018;山东省农业装备智能化工程实验室,泰安271018;山东农业大学机械与电子工程学院,泰安271018;山东农业大学机械与电子工程学院,泰安271018;山东省园艺机械与装备重点实验室,泰安271018 |
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| 基金项目： | 国家重点研发计划项目（2016YFD0201104）和国家苹果产业技术体系项目（CARS-27） |
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| 摘 要： | 为精准预测开花期、幼果期和果实膨大期苹果树叶片的氮含量，提出一种基于组合色彩特征的苹果树叶片氮含量预测模型。首先，获取苹果树叶片图像并提取R、G、B单色分量及14种色彩组合参数共计17种色彩特征，通过主成分分析提取不同时期苹果树叶片氮含量关键影响因子，消除原始变量之间的相关性，降低模型输入向量维度；其次，对建立的PCA-SVM、PCA-BP、PCA-ELM预测模型在不同时期对苹果树叶片氮含量预测效果与精度进行对比，得到不同时期最佳的预测模型；最后，利用最佳预测模型对不同时期苹果树叶片氮含量进行预测，并通过自适应遗传算法对最佳预测模型参数进行优化。试验结果表明：在不同生长时期，PCA-SVM模型的预测精度均高于PCA-BP、PCA-ELM模型；优化后PCA-SVM预测模型在开花期、幼果期和果实膨大期的平均绝对误差分别为0.640、0.558、0.544g/kg，平均绝对百分误差分别为0.057、0.050、0.064g/kg，均方根误差分别为0.800、0.747、0.737g/kg，优于优化前预测模型。该模型具有良好的预测性能和泛化能力，可以为果园精准施肥管理、提升果品品质、避免资源浪费和环境污染提供理论依据。
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| 关 键 词： | 苹果树叶片 氮含量预测 色彩特征 主成分分析 支持向量机 |
| 收稿时间： | 2020/11/10 0:00:00 |
Prediction of Nitrogen Content in Apple Leaves in Each Growth Period Based on Combined Color Characteristics |
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| WANG Jinxing,LIU Xuemei,LIU Shuangxi,QUAN Zekun,XU Chunbao,JIANG Hao.Prediction of Nitrogen Content in Apple Leaves in Each Growth Period Based on Combined Color Characteristics[J].Transactions of the Chinese Society of Agricultural Machinery,2021,52(10):272-281，376. |
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| Authors: | WANG Jinxing LIU Xuemei LIU Shuangxi QUAN Zekun XU Chunbao JIANG Hao |
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| Institution: | Shandong Agricultural University |
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| Abstract: | In order to accurately predict the nitrogen content in different scales of apple leaves at flowering, young fruit and fruit expansion periods, a combined color characteristics based prediction model of apple leaf nitrogen content was proposed. Firstly, the image of apple leaves was obtained and 17 color features, including R,G,B monochromatic components and 14 color combination parameters were extracted, and the key influencing factors of nitrogen content of apple leaves in different periods were extracted by principal component analysis to eliminate the correlation between the original variables and reduce the input vector dimension of the model. Secondly, the PCA-SVM, PCA-BP and PCA-ELM prediction models were established in different periods, the prediction effect and accuracy of apple leaf nitrogen content were compared, and the best prediction model in different periods was obtained. Finally, the best prediction model was used to predict the nitrogen content of apple leaves in different periods, and the parameters of the best prediction model were optimized by adaptive genetic algorithm. The results showed that the prediction accuracy of PCA-SVM model was higher than that of PCA-BP and PCA-ELM model in different growth periods; the mean absolute error of PCA-SVM prediction model in flowering period, young fruit period and fruit expansion period was 0.640 g/kg, 0.558 g/kg and 0.544 g/kg, and mean absolute percentage error was 0.057 g/kg, 0.050 g/kg and 0.064 g/kg, and root mean square error was 0.800g/kg, 0.747 g/kg and 0.737 g/kg, which was better than that of the prediction model before optimization. The model had good prediction performance and generalization ability, which can provide theoretical basis for orchard precision fertilization management, improving fruit quality, avoiding resource waste and environmental pollution. |
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| Keywords: | apple leaf nitrogen content prediction color features PCA SVM |
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