|
|
|
|
|
|
| Research on the Chlorophyll Content (SPAD) Distribution Based on the Consumer-Grade Modified Near-Infrared Camera |
| ZHANG Jian1, 2, MENG Jin1, 2, ZHAO Bi-quan1, 2, ZHANG Dong-yan3, XIE Jing4* |
1. College of Resources and Environmental Sciences, Huazhong Agricultural University, Wuhan 430070, China
2. Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtse River), Ministry of Agriculture, Wuhan 430070, China
3. Anhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, Hefei 230601, China
4. College of Science, Huazhong Agricultural University, Wuhan 430070, China |
|
|
|
|
Abstract Convenient and reliable crop nutrition diagnosis methods is basis of scientific crop fertilizer management and the core of the precision agriculture, and chlorophyll content is an important index of crop nitrogen nutrition content. In this research, the research object was rice leaf, and visible image and the center wavelength of 650, 680, 720, 760, 850 and 950 nm near infrared image were captured by transformed ordinary camera and filters. Then the relative reflectance values of different wave band were acquired. After regression analysis with visible-band and near-infrared band combined, the high precision and stable models were selected. Compared with the three imaging channels of camera, the correlation between chlorophyll content (SPAD value) and R channel was higher than B, G channels. Results showed that in the comparison of vegetation indexes, GVI can best reflect growth status of crops, and 760 nm has become the best near-infrared band in SPAD prediction. The model prediction accuracy R2 of the least squares support vector machine method combined with multiple vegetation index was 0.831 4, while ideal result had been achieved. Meanwhile, hyperspectral image of rice leaf was captured by hyperspectral imager. Compared the two imaging modalities, the multi factor prediction model based on vegetation index has the same precision. Experiments proved that consumer-grade near infrared camera could gain similar estimation result of chlorophyll content as hyperspectral imager.
|
|
Received: 2017-06-05
Accepted: 2017-11-22
|
|
|
|
Corresponding Authors:
XIE Jing
E-mail: xiejing625@mail.hzau.edu.cn
|
|
[1] ZHANG Yan-chao，ZHUANG Zai-chun, XIAO Yu-zhao，et al(张艳超，庄载椿，肖宇钊，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2015，31(17)：207.
[2] Chen C, Liao G P, Shi X H, et al. Journal of Hunan Agricultural University, 2011, 37(5): 474.
[3] JIA Biao，MA Fu-yu(贾 彪，马富裕). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报)，2016，47(3)：305.
[4] WANG Juan，WEI Chang-zhou，WANG Xiao-juan，et al(王 娟，危常州，王肖娟，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2013，29(24)：173.
[5] Yang W, Sigrimis N, Li M, et al. Correlations between Nitrogen Content and Multispectral Image of Greenhouse Cucumber Grown in Different Nitrogen Level. Computer and Computing Technologies in Agriculture VI，2012. 456.
[6] GUO Wei，ZHANG Yan-e，ZHU Jing-fu，et al(郭 威，张彦娥，朱景福，等). Journal of Agricultural Mechanization Research(农机化研究)，2011，33(10)：31.
[7] WU Qian，SUN Hong，LI Min-zan(吴 倩，孙 红，李民赞). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2015，35(1)：178.
[8] XIE Jing，CHEN Shi，WANG Jun-ke，et al(谢 静，陈 适，王珺珂，等). Journal of Central China Normal University·Natural Sciences(华中师范大学学报·自然科学版)，2014，48(2)：269.
[9] LIU Ren-jie，FANG Jun-long，LI Min-zan，et al(刘仁杰，房俊龙，李民赞，等). Journal of Agricultural Mechanization Research(农机化研究)，2016，4：141.
|
| [1] |
SHI Wen-qiang1, XU Xiu-ying1*, ZHANG Wei1, ZHANG Ping2, SUN Hai-tian1, 3, HU Jun1. Prediction Model of Soil Moisture Content in Northern Cold Region Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1704-1710. |
| [2] |
YANG Xu, LU Xue-he, SHI Jing-ming, LI Jing, JU Wei-min*. Inversion of Rice Leaf Chlorophyll Content Based on Sentinel-2 Satellite Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 866-872. |
| [3] |
TANG Yu-zhe, HONG Mei, HAO Jia-yong, WANG Xu, ZHANG He-jing, ZHANG Wei-jian, LI Fei*. Estimation of Chlorophyll Content in Maize Leaves Based on Optimized Area Spectral Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 924-932. |
| [4] |
NIU Teng1, 3, LU Jie1, 2*, YU Jia-xin4, WU Ying-da5, LONG Qian-qian3, YU Qiang3. Research on Inversion of Water Conservation Distribution of Forest Ecosystem in Alpine Mountain Based on Spectral Features[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 530-536. |
| [5] |
ZHAO Yu-hui，LIU Xiao-dong，ZHANG Lei，LIU Yong-hong. Research on Calibration Transfer Method Based on Joint Feature Subspace Distribution Alignment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3411-3417. |
| [6] |
LI Li-jie1，2, YUE Yan-bin2, WANG Yan-cang3, ZHAO Ze-ying2, LI Rui-jun2, NIE Ke-yan2, YUAN Ling1*. The Quantitative Study on Chlorophyll Content of Hylocereus polyrhizus Based on Hyperspectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3538-3544. |
| [7] |
LIU Tan1, 2, XU Tong-yu1, 2*, YU Feng-hua1, 2, YUAN Qing-yun1, 2, GUO Zhong-hui1, XU Bo1. Chlorophyll Content Estimation of Northeast Japonica Rice Based on Improved Feature Band Selection and Hybrid Integrated Modeling[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2556-2564. |
| [8] |
Nigela Tuerxun1, Sulei Naibi2, GAO Jian3, SHEN Jiang-long1, ZHENG Jiang-hua1*, YU Dan-lin4. Chlorophyll Content Estimation of Jujube Leaves Based on GWLS-SVR Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1730-1736. |
| [9] |
LIU Shuang, YU Hai-ye, ZHANG Jun-he, ZHOU Hai-gen, KONG Li-juan, ZHANG Lei, DANG Jing-min, SUI Yuan-yuan*. Study on Inversion Model of Chlorophyll Content in Soybean Leaf Based on Optimal Spectral Indices[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1912-1919. |
| [10] |
HAN Qian-qian, YANG Ke-ming*, LI Yan-ru, GAO Wei, ZHANG Jian-hong. SVD-ANFIS Model for Predicting the Content of Heavy Metal Lead in Corn Leaves Using Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1930-1935. |
| [11] |
LI Xin-xing1, GUO Wei1, BAI Xue-bing1, YANG Ming-song2*. Review on the Application of Spectroscopy Technology in Aquatic Product Quality Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(05): 1343-1349. |
| [12] |
ZHANG Jian-hong, YANG Ke-ming*, HAN Qian-qian, LI Yan-ru, GAO Wei. Predicting the Copper Pollution Information of Corn Leaves Spectral Based on an IWD-Hankel-SVD Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(05): 1505-1512. |
| [13] |
LIU Jian1, LAO Chang-ling2, YUAN Jing3, SUN Meng-he4, LUO Li-qiang5, SHEN Ya-ting5*. Recent Progress in the Application of X-Ray Spectrometry in Biology and Ecological Environment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 675-685. |
| [14] |
CHANG Tao, XIE Xin, GUAN Mei, ZHANG Qiu-ping, ZHANG Zhen-qian*, GUAN Chun-yun. Analysis of the Relationship Between Hyperspectral Reflectance and Yield of Rape Under Different Micro Fertilizer Conditions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 552-557. |
| [15] |
LIU Hao-ran1, HU Qi-hou2*, TAN Wei2, SU Wen-jing3, CHEN Yu-jia2, ZHU Yi-zhi2, LIU Jian-guo2. Study of the Urban NO2 Distribution and Emission Assessment Based on Mobile MAX-DOAS Observations[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 11-19. |
|
|
|
|
