A systematic study on bio-inspired frameworks for fertilizer optimization
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Abstract
Fertilizers supply nutrients to the soil to intensify soil fertility and improve plant growth. Fertilizer application to the crops was optimized by identifying the optimal fertilizer requirement as per the soil type and encouraging the usage of manure. Complex fertilizers contain multiple nutrients in each individual granule. They are cost-effective as well as highly available, when compared to manure, but they always lead to imbalance of nutrients, either in excess or in shortage. Excessive usage of chemical nutrients affects the soil quality and also affects the ecology. To avoid the imbalance in nutrients, manure application is suggested in this work along with fertilizers. The research finding in fertilizer optimization is performed by using two well-known optimization algorithms, Fruit Fly Optimization (FFO) algorithm and Social Spider Algorithm (SSA) inspired from the biological species, fruit fly and spider. Agricultural region in Coimbatore district, situated in the state of Tamil Nadu, India Results found that excess application of fertilizer brought down with systematic optimization plans, through the harmful influences of fertilizers can be avoided to a greater extent
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References
Bio-organic manure (n.d.). Retrieved from https://bioorganicfertilizer.wordpress.com/types-of-manure/
Brownlee, J. (2005). On Biologically Inspired Computation a.k.a. The Field, Technical Report 5-02, Swinburne University of Technology.
Choubey, N. S. (2014). Fruit Fly Optimization Algorithm for Travelling Salesperson Problem. International Journal of Computer Applications (0975 – 8887), 107:1-6.
Chun-Li, W., Shiuan-Yuh, C. and Chiu-Chung, Y. (2014). Present situation and future perspective of biofertilizer for environmentally friendly agriculture. Annual Reports, 1-5.
Coimbatore district, irrigation. Retrieved from http://www.coimbatore.nic.in/pdf/ SHB004.pdf
Coimbatore district, soil classification. Retrieved from http://www.coimbatore.nic.in/pdf/ SHB003.pdf.
Cuevas, E., Cienfuegos, M., Zaldívar, D. and Pérez-Cisneros, M. (2013). A swarm optimization algorithm inspired in the behavior of the social-spider, Expert Systems with Applications, 40:6374-6384.
Darwish, A. (2018). Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications. Future Computing and Informatics Journal, 3:231-246
Effects of chemical fertilizer. Retrieved from http://www.sustainablebabysteps.com/effects-of-chemical-fertilizers.html
Environmental Studies (2013). Retried from http://mjcetenvsci.blogspot.in/2013/10/effects-of-modern-agriculture.html.
Feng, X., Lau, F. C. M. and Yu, H. (2013). A Novel Bio-inspired Approach Based on the Behavior of Mosquitoes. Information Sciences, 233:87-108.
Guo, Y., Zhang, M., Liu, Z., Zhao, C., Lu, H., Zheng, L. and Li, Y. C. (2020). Applying and Optimizing Water-Soluble, Slow-Release Nitrogen Fertilizers for Water-Saving Agriculture, ACS Omega, 5:11342-11351.
Harrison Rware (2014). Fertilizer Optimization Tool an innovation for resource poor farmers in Uganda, OFRA.
Itelima, J. U., Bang, W. J., Sila, M. D., Onyimba, I. A. and Egbere, O. J. A. (2018). A review: Biofertilizer - A key player in enhancing soil fertility and crop productivity. Journal of Microbiology and Biotechnology Reports, 2:22-28.
Keplinger, K. O. and Hauck, L. M. (2006). The Economics of Manure utilization: Model and Application. Journal of Agricultural and Resource Economics, 31:414-440.
Knobeloch, L., Salna, B. and Hogan, A. (2009). Blue babies and Nitrate contaminating well water. Journal of Science, 2:6-24.
Liang, J. J., Pan, Q. K. and Chen, T. J. (2010). A Dynamic Multi-swarm Particle Swarm Optimizer for Blocking Flow Shop Scheduling. 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA). DOI: 10.1109/BICTA.2010.5645309
Macabiog, R. E. N., Fadchar, N. A. and Cruz, J. C. D. (2020). Soil NPK Levels Characterization Using Near Infrared and Artificial Neural Network. 16th IEEE International Colloquium on Signal Processing & its Applications, 2020:28-29.
Masrie, M., Rosman, M. S. A., Sam, R. and Janin, Z. (2017). Detection of Nitrogen, Phosphorus, and Potassium (NPK) nutrients of soil using Optical Transducer, Proc. of the 4th IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA) 28-30 November.
Pan, W. T. (2012). A new fruit fly optimization algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 26:69-74.
Panigrahi, B. K., Shi, Y. and Lim, M. H. (2011). Handbook of Swarm Intelligence. Series: Adaptation, Learning, and Optimization, Vol 7, Springer-Verlag Berlin Heidelberg, ISBN 978-3-642-17389-9.
Shan, D., Cao, G. H. and Dong, H. J. (2013). LGMS-FOA: An Improved Fruit Fly Optimization Algorithm for solving Optimization Problems, Mathematical Problems in Engineering, 2013:1-9.
Singh, H. and Sharma, N. (2014). Optimization of fertilizer rates for wheat crop using fuzzy expert system. International Journal of Computer Applications, 100:36-40.
Sivakumar, N., Amudha, T. and Thilagavathi, N. (2019). Development of a Novel Bio Inspired Framework for Fertilizer Optimization. 2019 Amity International Conference on Artificial Intelligence (AICAI), 2019:175-181.
Soil and plant nutrient testing lab (2017). Retrieved from https://ag.umass.edu/soil-plant-tissue-testing-lab/fact-sheets/over-fertilization-of-soils-its-causes-effects remediation. [Accessed: 2017].
Thilagavathi, N. and Amudha, T. (2019). A novel methodology for optimal land allocation for agricultural crops using Social Spider Algorithm. PeerJ, 7:e7559 https://doi.org/10.7717/peerj.7559.
Thilagavathi, N., Amudha, T. and Sivakumar, N. (2017). Computational perspective on organic farming – A Survey. 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), 24-29.
Thilagavathi, N., Ramakrishnan, S. and Amudha, T. (2021). A Novel Bio-inspired Optimization Framework for Effective Crop Land Allocation and Utilization. In: 2nd International Conference on Intelligent Engineering and Management (ICIEM), 2021:182-187. doi: 10.1109/ICIEM51511.2021.9445317.
TNAU Agrotech portal, agriculture. Retrieved from http://www.agritech.tnau.ac.in/ agriculture/ agri_min_nutri_essentialelements.html.
Tsui, S. K. W. (2009). High-throughput DNA sequencing and bioinformatics: Bottlenecks and opportunities. IEEE International Conference on Granular Computing. 1: DOI Bookmark: 10.1109/GRC.2009.5255117
Yang, X. S. (2009). Firefly algorithms for multimodal optimization, in: Stochastic Algorithms: Foundations and Applications, SAGA 2009, Lecture Notes in Computer Sciences, 5792:169-178.