UIJRT » United International Journal for Research & Technology

Nitrogen Deficiency Detection in Paddy for Urea Fertilizer Management

P Jagadish, Anand R, M Merlyn Mercylona, M Aishwarya and Namrata Karki

Total Views / Downloads: 184 

Cite ➜

Jagadish, P., Anand, R., Mercylona, M.M., Aishwarya, M. and Karki, N., 2021. Nitrogen Deficiency Detection in Paddy for Urea Fertilizer Management. United International Journal for Research & Technology (UIJRT), 2(7), pp.55-57.

Abstract

Plants need nutrients to survive and thrive. It is essential that they receive the right type and quantity of nutrients at the right time. Nitrogen is the major nutrient involved in growth of paddy crops and is the most common nutrient in commercial fertilizers. These fertilizers are added based on predefined values which are based on land size, age of the crop, and is usually recommended by the fertilizer seller. This approach is not sustainable as it does not account for the soil fertility level. This results in mismanagement and over fertilization which has detrimental effects to the environment. Soil fertility, and understanding the various nutrient levels is key in proper plant growth, as too much of a particular kind can have detrimental effects on plant growth, by preventing uptake of other essential nutrients. Lack of nutrients is also problematic as it can stunt plant growth. Currently soil testing by sending samples to labs is the well-established practice, which is time consuming and generally not followed for every growing season. Paddy is one of the staple cereal crops in India, and largely consumed in south India. It is nutrient demanding crop, which need good levels of major nutrients, especially nitrogen for high yield.  In this paper we review the recent technologies used to estimate and identify nitrogen deficiency levels in paddy crops. Computer models are capable of classifying plants as healthy and not healthy, and identify nitrogen deficiencies, using image processing, machine learning, and deep learning techniques.

Keywords: Nitrogen, Urea, Fertilizer, Management.

References

  1. S. Anami, N. N. Malvade, and S. Palaiah, “Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images,” Artificial Intelligence in Agriculture, vol. 4, pp. 12–20, 2020, doi: 10.1016/j.aiia.2020.03.001. [1]
  2. K. Sethy, N. K. Barpanda, Amiya Rath, S. K. Behera, “Nitrogen deficiency prediction of rice crop based on convolutional neural network”, Journal of Ambient Intelligence and Humanized Computing, Nov. 2020, DOI: 10.1007/s12652-020-01938-8 [2]
  3. A. D. Siddik, Md. H. R. Sohag, and A. U. Zaman, “Smart LCC Device: LCC-Based IoT Device for measuring urea consumption in major food crops,” 2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), Nov. 2019, doi: 10.1109/raaicon48939.2019.72 [3]
  4. Torikul Islam, R. U. B. Rizan, Yeasir Arefin Tusher, “Nitrogen fertilizer recommendation for paddies through automating the leaf color chart (LCC), International Journal of Advanced Computer Science and Applications, Vol. 11, No. 8, Jan. 2020, DOI: 10.14569/IJACSA.2020.0110891 [4]
  5. Latte, S. Shidnal, and B. Anami, “Rule Based Approach to Determine Nutrient Deficiency in Paddy Leaf Images,” International Journal of Agricultural Technology, vol. 13, no. 2, pp. 227–245, 2017.

For Conference & Paper Publication​

UIJRT Publication - International Journal