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| 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 |
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