Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages.

Abstract

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.

Plant phenotyping relevance

デジタル画像処理と特徴抽出、K-Means、BPNNを用いて白菜集団の窒素含量および群体品質を推定・評価するモデルを構築しており、植物形質の取得・推定手法が研究の中心である。

Code and data availability

The article states data are within the paper and its Supporting information files, but no paper-specific public dataset, image collection, code repository, or model checkpoint with an authors' public URL is provided in the supplied blocks. The only URLs are generic journal/editorial links, and the paper was retracted.

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