Water and nitrogen in-situ imaging detection in live corn leaves using near-infrared camera and interference filter
Abstract Background Realizing imaging detection of water and nitrogen content in different regions of plant leaves in-site and real-time can provide an efficient new technology for determining crop drought resistance and nutrient regulation mechanisms, or for use in precision agriculture. Near-infra...
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BMC
2021
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oai:doaj.org-article:5531b010ffdd492a9bf381e4e847a0a02021-11-14T12:11:17ZWater and nitrogen in-situ imaging detection in live corn leaves using near-infrared camera and interference filter10.1186/s13007-021-00815-51746-4811https://doaj.org/article/5531b010ffdd492a9bf381e4e847a0a02021-11-01T00:00:00Zhttps://doi.org/10.1186/s13007-021-00815-5https://doaj.org/toc/1746-4811Abstract Background Realizing imaging detection of water and nitrogen content in different regions of plant leaves in-site and real-time can provide an efficient new technology for determining crop drought resistance and nutrient regulation mechanisms, or for use in precision agriculture. Near-infrared imaging is the preferred technology for in-situ real-time detection owing to its non-destructive nature; moreover, it provides rich information. However, the use of hyperspectral imaging technology is limited as it is difficult to use it in field because of its high weight and power. Results We developed a smart imaging device using a near-infrared camera and an interference filter; it has a low weight, requires low power, and has a multi-wavelength resolution. The characteristic wavelengths of the filter that realize leaf moisture measurement are 1150 and 1400 nm, respectively, the characteristic wavelength of the filter that realizes nitrogen measurement is 1500 nm, and all filter bandwidths are 25 nm. The prediction result of the average leaf water content model obtained with the device was R2 = 0.930, RMSE = 1.030%; the prediction result of the average nitrogen content model was R2 = 0.750, RMSE = 0.263 g. Conclusions Using the average water and nitrogen content model, an image of distribution of water and nitrogen in different areas of corn leaf was obtained, and its distribution characteristics were consistent with the actual leaf conditions. The experimental materials used in this research were fresh leaves in the field, and the test was completed indoors. Further verification of applying the device and model to the field is underway.Ning ZhangPeng-cheng LiHubin LiuTian-cheng HuangHan LiuYu KongZhi-cheng DongYu-hui YuanLong-lian ZhaoJun-hui LiBMCarticleCorn leafMultispectral imagingNear infrared cameraNear-infraredNitrogen contentWater contentPlant cultureSB1-1110Biology (General)QH301-705.5ENPlant Methods, Vol 17, Iss 1, Pp 1-11 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Corn leaf Multispectral imaging Near infrared camera Near-infrared Nitrogen content Water content Plant culture SB1-1110 Biology (General) QH301-705.5 |
| spellingShingle |
Corn leaf Multispectral imaging Near infrared camera Near-infrared Nitrogen content Water content Plant culture SB1-1110 Biology (General) QH301-705.5 Ning Zhang Peng-cheng Li Hubin Liu Tian-cheng Huang Han Liu Yu Kong Zhi-cheng Dong Yu-hui Yuan Long-lian Zhao Jun-hui Li Water and nitrogen in-situ imaging detection in live corn leaves using near-infrared camera and interference filter |
| description |
Abstract Background Realizing imaging detection of water and nitrogen content in different regions of plant leaves in-site and real-time can provide an efficient new technology for determining crop drought resistance and nutrient regulation mechanisms, or for use in precision agriculture. Near-infrared imaging is the preferred technology for in-situ real-time detection owing to its non-destructive nature; moreover, it provides rich information. However, the use of hyperspectral imaging technology is limited as it is difficult to use it in field because of its high weight and power. Results We developed a smart imaging device using a near-infrared camera and an interference filter; it has a low weight, requires low power, and has a multi-wavelength resolution. The characteristic wavelengths of the filter that realize leaf moisture measurement are 1150 and 1400 nm, respectively, the characteristic wavelength of the filter that realizes nitrogen measurement is 1500 nm, and all filter bandwidths are 25 nm. The prediction result of the average leaf water content model obtained with the device was R2 = 0.930, RMSE = 1.030%; the prediction result of the average nitrogen content model was R2 = 0.750, RMSE = 0.263 g. Conclusions Using the average water and nitrogen content model, an image of distribution of water and nitrogen in different areas of corn leaf was obtained, and its distribution characteristics were consistent with the actual leaf conditions. The experimental materials used in this research were fresh leaves in the field, and the test was completed indoors. Further verification of applying the device and model to the field is underway. |
| format |
article |
| author |
Ning Zhang Peng-cheng Li Hubin Liu Tian-cheng Huang Han Liu Yu Kong Zhi-cheng Dong Yu-hui Yuan Long-lian Zhao Jun-hui Li |
| author_facet |
Ning Zhang Peng-cheng Li Hubin Liu Tian-cheng Huang Han Liu Yu Kong Zhi-cheng Dong Yu-hui Yuan Long-lian Zhao Jun-hui Li |
| author_sort |
Ning Zhang |
| title |
Water and nitrogen in-situ imaging detection in live corn leaves using near-infrared camera and interference filter |
| title_short |
Water and nitrogen in-situ imaging detection in live corn leaves using near-infrared camera and interference filter |
| title_full |
Water and nitrogen in-situ imaging detection in live corn leaves using near-infrared camera and interference filter |
| title_fullStr |
Water and nitrogen in-situ imaging detection in live corn leaves using near-infrared camera and interference filter |
| title_full_unstemmed |
Water and nitrogen in-situ imaging detection in live corn leaves using near-infrared camera and interference filter |
| title_sort |
water and nitrogen in-situ imaging detection in live corn leaves using near-infrared camera and interference filter |
| publisher |
BMC |
| publishDate |
2021 |
| url |
https://doaj.org/article/5531b010ffdd492a9bf381e4e847a0a0 |
| work_keys_str_mv |
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