Estimation of cotton yield with varied irrigation and nitrogen treatments using aerial multispectral imagery
Keywords:
remote sensing, multispectral imagery, cotton, yield, nitrogen, irrigation, soil propertiesAbstract
Cotton yield varies spatially within a field. The variability can be caused by various production inputs such as soil properties, water management, and fertilizer application. Airborne multispectral imaging is capable of providing data and information to study effects of the inputs on yield qualitatively and quantitatively in a timely and cost-effective fashion. A 10-ha cotton field with irrigation and non-irrigation 2×2 blocks was used in this study. Six nitrogen application treatments were randomized with two replications within each block. As plant canopy was closed, airborne multispectral images of the field were acquired using a 3-CCD MS4100 camera. The images were processed to generate various vegetation indices. The vegetation indices were evaluated for the best performance to characterize yield. The effect of irrigation on vegetation indices was significant. Models for yield estimation were developed and verified by comparing the estimated and actual yields. Results indicated that ratio of vegetation index (RVI) had a close relationship with yield (R2=0.47). Better yield estimation could be obtained using a model with RVI and soil electrical conductivity (EC) measurements of the field as explanatory variables (R2=0.53). This research demonstrates the capability of aerial multispectral remote sensing in estimating cotton yield variation and considering soil properties and nitrogen.References
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S, Daughtry C S T, et al. Remote sensing for crop management. Photogrammetric Engineering and Remote Sensing, 2003; 69(6): 647-664.
[2] Ajayi A E, Olufayo A A. Evaluation of two temperature stress indices to estimate grain sorghum yield and evapotranspiration. Agronomy Journal, 2004; 96: 1282- 1287.
[3] Singh R. Application of remote sensing technology for crop yield estimation. Journal of the Indian Society of Agricultural Statistics, 2004; 57: 226-246.
[4] Yang C, Everitt J H, Bradford J M. Comparison of QuickBird satellite imagery and airborne imagery for mapping grain sorghum yield patterns. Precision Agriculture, 2006; 7: 33-44.
[5] Yang C, Everitt J H, Bradford J M. Evaluating high resolution QuickBird satellite imagery for estimating cotton yield. Transactions of the ASAE, 2006; 49(5): 1599-1606.
[6] Hunsaker D J, Barnes E M, Clarke T R, Fitzgerald G J, Pinter P J. Cotton irrigation scheduling using remotely sensed and FAO-56 basal crop coefficients. Transactions of the ASAE, 2005; 48(4): 1395-1407.
[7] Detar W R, Penner J V, Funk H A. Airborne remote sensing to detect plant water stress in full canopy cotton. Transactions of the ASABE, 2006; 49(3): 655-665.
[8] Panda S S, Ames D P, Panigrahi P. Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sensing, 2010; 2: 673-696.
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Published
2013-06-18
How to Cite
Huang, Y., Sui, R., Thomson, S. J., & Fisher, D. K. (2013). Estimation of cotton yield with varied irrigation and nitrogen treatments using aerial multispectral imagery. International Journal of Agricultural and Biological Engineering, 6(2), 37–41. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/706
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Information Technology, Sensors and Control Systems
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