Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages.
Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages.
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DOI：
10.1371/journal.pone.0248923
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Shao X
中科院分区：
文献类型：
--
作者：
Wang Q;Mao X;Jiang X;Pei D;Shao X
目的是监测作物氮素利用效率，智能评价作物在生产过程中对养分的吸收。研究对象为大白菜。基于数字图像处理技术，通过不同密度和氮肥施用量构建不同农业参数的大白菜群体，并建立估算NC （nitrogen Content）模型。采用特征提取方法，通过K-Means聚类算法对大白菜种群进行分类，构建大白菜种群质量反向传播神经网络（BPNN）模型。研究了不同农业参数与人口素质之间的非线性映射关系，以及各指标的贡献率。对大白菜氮素利用进行了有效的监测。结果表明，该模型在不同生长阶段的相关系数均在0.70以上。该模型能较准确地估计大白菜群体的NC。大白菜种群质量BPNN模型的结果表明，基于苗数的大白菜种群种植密度是合理的。所构建的群体质量评价模型对不同时期的大白菜质量评价具有较高的R2值和较低的RMSE（均方根误差）值，表明该模型适用于大白菜不同生育期的群体质量评价。所构建的氮素利用模型和质量评价模型可以监测作物不同生育期的养分利用情况，确定不同生育期其他产量组的农业特征，明确不同生育期农业参数的表现。上述结果可为作物生长智能检测提供一些思路。
The purposes are to monitor the nitrogen utilization efficiency of crops and intelligently evaluate the absorption of nutrients by crops during the production process. The research object is Chinese cabbage. The Chinese cabbage population with different agricultural parameters is constructed through different densities and nitrogen fertilizer application rates based on digital image processing technology, and an estimation NC (Nitrogen Content) model is established. The population is classified through the K-Means Clustering algorithm using the feature extraction method, and the Chinese cabbage population quality BPNN (Backpropagation Neural Network) model is constructed. The nonlinear mapping relationship between different agricultural parameters and population quality, and the contribution rate of each indicator, are studied. The nitrogen utilization of Chinese cabbage is monitored effectively. Results demonstrate that the proposed NC estimation model has correlation coefficients above 0.70 in different growth stages. This model can accurately estimate the NC of the Chinese cabbage population. The results of the Chinese cabbage population quality BPNN model show that the population planting density based on the seedling number is reasonable. The constructed population quality evaluation model has a high R2 value and a comparatively low RMSE (Root Mean Square Error) value for the quality evaluation of Chinese cabbage in different periods, showing that it applies to evaluate the population quality of Chinese cabbage in different growth stages. The constructed nitrogen utilization model and quality evaluation model can monitor the nutrient utilization of crops in different growth stages, ascertain the agricultural characteristics of other yield groups in different growth stages, and clarify the performance of agricultural parameters in different growth stages. The above results can provide some ideas for crop growth intelligent detection.
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影响因子：
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作者：
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