Development and application of crop monitoring system for detecting chlorophyll content of tomato seedlings
Keywords:
multi-spectral image, crop growth status, image acquisition, 2-CCD sensor, precision agricultureAbstract
A crop monitoring system was developed to nondestructively monitor the crop growth status in the field. With a two channel multispectral camera with one lens, controlling platform, wireless remote control module and control software, the system was able to synchronously acquire visible image (red(R), green(G), blue(B): 400-700 nm) and near-infrared (NIR) image (760-1 000 nm). The tomato seedlings multi-spectral images collection experiment in the greenhouse was conducted by using the developed system from the seeding stage to fruiting stage. More than 240 couples of tomato seedlings pictures were acquired with the Soil and Plant Analyzer Development (SPAD) value measured at the same time. The obtained images were available to process, and some vegetation indexes, such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI) and normalized difference green index (NDGI), were calculated. Considering the SPAD value and the correlation coefficient between SPAD and other parameters in different fertilization treatments, the multiple linear regressions (MLR) model for tomato seedlings chlorophyll content predication was built based on the average gray value in red, green, blue and NIR, vegetable indexes, NDVI, RVI and NDGI in the 33.3% (N1), 66.6% (N2), and 100% (N3) nutrient levels during seeding stage and blossom and fruit stage. The R2 of the model is 0.88. The results revealed that the developed crop monitoring system provided a feasible tool to detect the growth status of tomato. More filed experiments and multi-spectral image analysis will be investigated to evaluate the crop growth status in the near future. Keywords: multi-spectral image, crop growth status, image acquisition, 2-CCD sensor, precision agriculture DOI: 10.3965/j.ijabe.20140702.017 Citation: Wu Q, Sun H, Li M Z, Yang W. Development and application of crop monitoring system for detecting chlorophyll content of tomato seedlings. Int J Agric & Biol Eng, 2014; 7(2): 138-145.References
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[2] Zhao D, Reddy K R, Kakani V G, Reddy V R. Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum. European Journal of Agronomy, 2005; 22(4): 391-403.
[3] Mahlein A K, Rumpf T, Welke P, Dehne H W, Plümer L, Steiner U, et al. Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment, 2013; 128: 21-30.
[4] Liu H B, Zhang Y G, Li Z H, Zhang C Y, Hu D Y. Application of canopy spectral reflectance in monitoring nitrogen status of winter wheat. Scientia Agricultura Sinica, 2004; 37(11): 1743-1748. (in Chinese with English abstract)
[5] Zhang J H, Wang K. New vegetation index for estimating nitrogen concentration using fresh leaf spectral reflectance. Transactions of the CSAE, 2008; 24(3): 158-161. (in Chinese with English abstract)
[6] Shen X, Zhang J, Jiang C H, Song Y. Correlation between chlorophyll content and spectral characteristics of soybean leaves. Soybean Science, 2009; 28(4): 747-750. (in Chinese with English abstract)
[7] Zhang J, Lv Y, Han C, Li D, Yao Z, Jiang X. New reflectance spectral vegetation indices for estimating rice nitrogen nutrition iii: development of a new vegetation index based on canopy red-edge reflectance spectra to monitor rice canopy leaf nitrogen concentration. Sensor Letters, 2011; 9: 1201–1206. (in Chinese with English abstract)
[8] Wang X, Li Z H, Yuan J, Fu J. The research between corn varieties canopy of NDVI and chlorophyll. Chinese Agricultural Science Bulletin, 2010; 28(16): 175-179. (in Chinese with English abstract)
[9] Ni J, Wang T T, Yao X, Cao W X, Zhu Y. Design and experiments of multi-spectral sensor for rice and wheat growth information. Journal of Agricultural Mechanization Research, 2013; 44(5): 207-212. (in Chinese with English
abstract)
[10] Govender M, Chetty K, Bulcock H. A review of hyperspectral remote sensing and its application in vegetation and water resource studies. Water Sa, 2007; 33(2): 145-151.
[11] Zhang H, Yao X G, Zhang X B, Zhu L L, Ye S T, Zheng K F, et al. Measurement of rice leaf chlorophyll and seed nitrogen contents by using multi-spectral imagine. Chinese Journal of Rice Science, 2008; 22(5): 555-558. (in Chinese with English abstract)
[12] Guo W, Zhang Y, Zhu J, Tang W, Zhang F, Zhao R. Combined optimization of tillage machine's frame base on Hyper Works. Journal of Agricultural Mechanization Research, 2011; 10: 31-39. (in Chinese with English abstract)
[13] Sun H, Li M, Zheng L, Zhang Y, Yang W. Evaluation of maize growth by ground based multi-spectral image, in: 2011 IEEE/SICE International Symposium on System Integration (SII). Presented at the 2011 IEEE/SICE International Symposium on System Integration (SII), 2011, 207-211.
[14] Wang H, Zhang Y, Guo W. Corn growth monitoring technology research based on multi-spectral images. Journal of Agricultural Mechanization Research, 2012; 11: 178-181. (in Chinese with English abstract)
[15] Kise M, Park B, Lawrence K C, Windham W R. Optical design and system calibration for three-band spectral imaging system with interchangeable filters. 2008 ASABE Annual International Meeting, 2008; No. 084685.
[16] Du L, Yi W, Zhang D, Fang W, Qiao Y. Multispectral image acquisition system based on liquid crystal tunable filter. Acta Optica Sinica, 2009; 29(1): 187-191. (in Chinese with English abstract)
[17] Matlab. From Wikipedia. Available at: http://en.wikipedia. org/wiki/Matlab.
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Qian, W., Hong, S., Minzan, L., & Wei, Y. (2014). Development and application of crop monitoring system for detecting chlorophyll content of tomato seedlings. International Journal of Agricultural and Biological Engineering, 7(2), 138–145. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/1220
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