International Journal for Asian Contemporary Research, 2(2): 50-57
Precision Nitrogen Management in Corn (Zea mays L.) Using Algorithm Based on the RGB Color Codes by Digital Imaging
Received: 17 July 2022 || Accepted: 20 August 2022 || Published: 01 September, 2022
A B S T R A C T
The study was carried out in the experimental field of the Department of Agronomy and Agricultural Extension, Rajshahi University, during the period from November 2017 to March 2018, to evaluate the precision nitrogen management in corn using an algorithm based on the RGB color code by digital imaging. The field experiment was set up using a split-plot experimental design with three replications. The experiment consists of three basal urea application rates (S1=100% of the standard basal dose of N; S2=75% of the standard basal dose of N, and S3=50% of the standard basal dose of N) and three topdressing urea application rates (N1=150% of standard topdressing dose of N; N2=100% of the standard topdressing dose of N and N3=50% of standard topdressing dose of N). Standard irrigation and other cultivation procedures were followed during the experiment. Considering different physiological responses, yield components, and yield of corn, it was found that the highest performance was noted for maximum top dressing urea rate (N1), which reduced gradually with the reduction of urea amount. The highest grain yield (9.61 t ha-1) was observed in N1, which was significantly reducedby 6.14 % for N2 and 9.26 % for N3. The highest stover yield (14.40 t ha-1) and biological yield (24.01 t ha-1) was found with N1. Harvest index (HI) was non-significant for both basal and top dressing urea application rate. The interaction between the basal and top dressing urea application rates had no discernible impact on physiological responses, yield-contributing traits, or maize yield.Our observations show nitrogen is the most important nutrient for maize productivity. While topdressing urea is in charge of maize development during the reproductive stage, basal urea is crucial for initial growth characteristics and hence delivers the highest stover yield.
Keywords: Corn, Nitogen management, Color code and Digital imaging.
Copyright information: Copyright © 2022 Author(s) retain the copyright of this article. This work is licensed under a Creative Commons Attribution 4.0 International License
To cite this article: Razzak, A., Biswas, B., Khan, T. A., Yasmin, N., Alam, A. M.S. and Islam, M. R. (2022). Precision Nitrogen Management in Corn (Zea mays L.) Using Algorithm Based on the RGB Color Codes by Digital Imaging. International Journal for Asian Contemporary Research, 2 (2): 50-57.
- Ali, M. M., Al-Ani, A., Eamus, D. and Tan, D. K. Y. 2013. An Algorithm Based on the RGB Colour Model to Estimate Plant Chlorophyll and Nitrogen Contents. International Conference on Sustainable Environment and Agriculture (IPCBEE). 57: 52-56.
- Amanullah, H. Rahman and Z. Shah. 2008. Effects of plant density and N on growth dynamics and light interception in maize. Archives of Agronomy and Soil Science, 54: 401-411.
- BARI (Bangladesh Agricultural Research Institute). Annual report for 2011-12. Bangladesh Rice Research Institute Gazipur, Bangladesh, https://www.barilibrary.org/index.php?p=show_detail&id=5771&keywords=
- Chandra shekara, C.P. 2009, Resource management in sugarcane (Saccharum officinarum L.) through drip irrigation, fertigation, planting pattern, and LCC based N application and area - production estimation through remote sensing. Ph.D..(Agri.)Thesis, Uni. Agric. Sci., Dharwad (India).
- Chowdhury, M.K And M.A. Islam. 1993. Production and uses of maize (in Banglali) Pub. by on Farm Des. Div. Bangladesh agril. Res. Inst. Joydebpur gazipur, Bangladesh. P.P 1-189
- Gonias, E. D., D. M. Oosterhuis and A. C. Bibi. 2011. Light interception and radiation use efficiency of okra and normal leaf cotton isolines. Environmental and Experimental Botany 72: 217–222.
- Gomez, K.A. and Gomez, A.A. 1984. Statistical Procedures for Agricultural Research. (2 edn.) John Wiley and Sons. Newyork. Chikester, Brisbane, Toronto. Singapore. p.680.
- Gulati A and Dixon, J .2008. Maize in Asia: Changing Markets and Incentives, Academic Foundation, New Delhi.
- Karim, Z., Hussain, S. G. and Ahmed, M. 1990. Salinity problems and crop intensification in the coastal regions of Bangladesh, BARC, Soil publication No. 33:63
- Kumar, R., Srinivas, K., Miah, M. M., Shah, H., Dahlan, H. A., & Qiu, H. (2014). Assessment of the maize situation, outlook and opportunities in Asia.
- Modhej, A., Kaihani, A., & Lack, S. (2014). Effect of nitrogen fertilizer on grain yield and nitrogen use efficiency in corn (Zea mays L.) hybrids under irrigated conditions. Proceedings of the National Academy of Sciences, India Section B: Biological Sciences, 84(3), 531-536.
- Moniruzzaman, M. S. Rahman M. S., Karim, M. K and Alam, Q. M. 2009.Economic analysis of maize production in Bangladesh. J. Agril. Res., 34(1):15-24.
- Moriri, S., Owoeye, L.G. and Mariga, I. K. 2010. Influence of component crop densities and planting patterns on maize production in dryland maize/cowpea intercropping systems. Afr. J. Agri. Res., 5 (11); 1200 -1207.
- Nelson, R.L. 2005. Tassel emergence and pollen shed. Corny news network.
- O’Neill, P.M., Shanahan, J.F., Schepers, J.S., Caldwell, B. 2004. Agronomic responses of corn hybrids from different eras to deficit and adequate levels of water and nitrogen.Agron. J. 96: 1660-1667.
- Shapiro, C.A.; Wortmann, C.S. Corn response to nitrogen rate, row spacing and plant density in Eastern Nebraska. Agron. J. 2006, 98, 529–535.
Article View: 1303 times