Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning for Brassica Campestris ssp. Chinensis L.
Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning for Brassica Campestris ssp. Chinensis L.
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DOI：
10.3390/s19112448
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Xin Xiong;Jingjin Zhang;Doudou Guo;Liying Chang;Danfeng Huang
中科院分区：
文献类型：
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作者：
Xin Xiong;Jingjin Zhang;Doudou Guo;Liying Chang;Danfeng Huang
及时准确地监测植物氮素（N）是精准施肥的关键。基于可见光的成像技术相对便宜且无处不在，开源分析工具已经激增。本研究以小白菜（Brassica campestris ssp.）chinensis L.）。在温室条件下，对盆栽小白菜进行4个氮素水平处理。他们的顶视图图像是在六个生长阶段使用相机获得的。相应的植物氮浓度测定破坏。采用随机森林（RF）、支持向量回归（SVR）和神经网络（NN）算法建立了氮营养指数（NNI）与基于图像的表型特征之间的定量关系。结果表明，基于颜色、纹理和几何相关特征的完整模型在预测NNI方面优于仅基于颜色相关特征的模型。RF全模型在苗期和收获期表现出最稳健的性能，分别达到0.823和0.943的预测精度。该模型的高预测精度允许低成本，非破坏性的监测N在精确的作物管理领域。
Monitoring plant nitrogen (N) in a timely way and accurately is critical for precision fertilization. The imaging technology based on visible light is relatively inexpensive and ubiquitous, and open-source analysis tools have proliferated. In this study, texture- and geometry-related phenotyping combined with color properties were investigated for their potential use in evaluating N in pakchoi (Brassica campestris ssp. chinensis L.). Potted pakchoi treated with four levels of N were cultivated in a greenhouse. Their top-view images were acquired using a camera at six growth stages. The corresponding plant N concentration was determined destructively. The quantitative relationships between the nitrogen nutrition index (NNI) and the image-based phenotyping features were established using the following algorithms: random forest (RF), support vector regression (SVR), and neural network (NN). The results showed the full model based on the color, texture, and geometry-related features outperforms the model based on only the color-related feature in predicting the NNI. The RF full model exhibited the most robust performance in both the seedling and harvest stages, reaching prediction accuracies of 0.823 and 0.943, respectively. The high prediction accuracy of the model allows for a low-cost, non-destructive monitoring of N in the field of precision crop management.