| 引用本文: | 马 淳,李世雄,达布希拉图,等.基于数字图像技术的西兰花氮素诊断施肥研究[J].灌溉排水学报,2026,45(3):35-45. |
| MA Chun,LI Shixiong,DABU Xilatu,et al.基于数字图像技术的西兰花氮素诊断施肥研究[J].灌溉排水学报,2026,45(3):35-45. |
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| 摘要: |
| 【目的】准确地诊断作物氮素营养状况，实现作物精准施肥和氮肥资源合理利用，利用数码图像参数预测作物氮素营养指标，明确氮素营养状况评价的最佳色彩参数和方程模型，指导作物氮素营养管理。【方法】通过设计5个不同水平的氮素梯度试验，分别为CK（不施用氮肥处理）、N1（施用172.5 kg/hm2氮肥处理）、N2（施用258 kg/hm2氮肥处理）、N3（施用345 kg/hm2氮肥处理）、N4（施用510 kg/hm2氮肥处理），分析西兰花氮素吸收规律并建立冠层图像的多元逐步回归模型，在考虑经济可行的前提下，以当地常规施肥和NDVI模型施肥为对照，进行田间诊断施肥验证。【结果】施氮量显著影响西兰花叶片吸氮量、产量及冠层色彩参数。西兰花的吸氮量随着施氮量的增加而增加，当施氮量＞258 kg/hm2时产量不再增加。利用Matlab 2018软件对冠层图像进行去躁和灰度化处理，再利用多元逐步回归方法对各颜色参量进行分析，B（蓝光值）与吸氮量之间存在显著正相关关系，R2达到0.863；比颜色指标S/H和S/V值与吸氮量之间也存在着正相关关系，R2分别达到0.697、0.737。通过试验获得施氮量和产量之间的关系，利用多元逐步回归的方法初步构建了基于数码相机拍摄冠层数字化指标（B值、S/H值以及S/V值）和NDVI值（归一化植被指数）的氮肥追施模型。通过验证试验发现，利用模型诊断施肥在补益村的施氮量下降13.1%~17.5%；秧郎村下降7.7%~18.3%；鲁溪村下降12.2%~19.4%。采用TOPSIS多准则决策模型，比较了不同模型施肥条件下的产量和施氮量，综合评价不同模型诊断施肥，表现为RGB模型＞HSV模型＞NDVI模型＞常规施肥。【结论】数字图像技术可应用于西兰花氮素营养诊断研究，最佳诊断施肥模型为RGB模型，作为一种新方法在西兰花氮素营养快速无损诊断、精准施肥及减少化肥施用量，在可持续农业中具有较好的应用潜力。 |
| 关键词: 西兰花；模型；氮素；产量 |
| DOI：10.13522/j.cnki.ggps.2025122 |
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| Imaging-based nitrogen diagnosis and fertilization management in broccoli production |
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MA Chun, LI Shixiong, DABU Xilatu, ZANG Chonghui, PENG Yuanyang
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1. Key Laboratory for Improving Quality and Productivity of Arable Land of Yunnan Province,
College of Resources and Environment, Yunnan Agricultural University, Kunming 650201, China;
2. College of Resources and Environment, Yunnan Agricultural University, Kunming 650201, China
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| Abstract: |
| 【Objective】Nitrogen is a key nutrient for broccoli growth, and inappropriate nitrogen management can reduce yield and increase environmental risks. Accurate, timely diagnosis of crop nitrogen status is thus essential for precision fertilization. This paper presents an imaging-based method for nitrogen diagnosis and fertilization management in broccoli production.【Method】An experiment with nitrogen application rates ranging from zero (CK) to 510 kg/hm2 was conducted in a broccoli field. In the experiment, broccoli nitrogen uptake and canopy images were collected. The relationship between nitrogen application rate and yield was established. A multiple stepwise regression model was developed to link canopy image parameters and nitrogen uptake, from which a topdressing nitrogen fertilization model was derived. Using conventional fertilization as the control, we experimentally investigated the efficacy of the fertilizer applications calculated using the image-based models in saving fertilizer at three sites.【Result】Nitrogen application significantly influenced leaf nitrogen uptake, yield and canopy color parameters. With increasing nitrogen application, crop nitrogen uptake increased, whereas yield plateaued when the application rate exceeded 258 kg/hm2. Multiple stepwise regression analysis showed a positive correlation between nitrogen uptake and the B (blue) value (R2=0.863), and between nitrogen uptake and the relative color indices S/H and S/V in the canopy images (R2=0.697 and 0.737, respectively). The validation experiment showed that the model-based fertilization practices reduced nitrogen application by 13.1%-17.5% at Site 1, 7.7%-18.3% at Site 2 and 12.2%-19.4% at Site 3, without compromising crop yield. We compared the yields of fertilizer applications calculated from different image-based models using the TOPSIS method, and the yields of different fertilizations was ranked in the order of RGB model＞HSV model＞NDVI model＞conventional fertilization.【Conclusion】Digital images can be used to diagnose nitrogen nutrition in broccoli canopy and help improve nitrogen fertilization. Among all image-based models we compared, the RGB model was the most effective; it can be used for non-destructive diagnosis of nitrogen status in plants, help optimize fertilization and reduce fertilizer application. |
| Key words: broccoli; model; nitrogen; yield |
