Sustainable Fertilizer Usage Optimizer for Higher Yield


Authors : Varunkumar B.; Madhan Raj M.; Midhun B M.; Poojitha M.; Parthasarathy E.; Prithika Sri S; Redhu Darsini G

Volume/Issue : Volume 9 - 2024, Issue 11 - November


Google Scholar : https://tinyurl.com/mubsb6pv

Scribd : https://tinyurl.com/ech8hknr

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV641

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : In modern agriculture, maximizing crop yield while maintaining soil health has become a critical challenge. This fertilizer recommendation app leverages precision agriculture techniques to provide farmers with tailored fertilizer recommendations that align with specific crop needs, soil conditions, and climate data. The app integrates data from soil testing, crop requirements, and weather patterns to offer optimized fertilizer plans that minimize waste and environmental impact while boosting productivity. By guiding users on optimal nutrient application, the app aims to reduce fertilizer misuse, lower costs, and promote sustainable farming practices. This user-friendly, mobile- compatible app supports multiple crops, local languages, and delivers actionable insights to improve agricultural efficiency across various farming scales.

References :

  1. Gebbers, R., & Adamchuk, V. I. (2010).Precision agriculture and food security.* Science, 327(5967), 828-831. This paper discusses the impact of precision agriculture technologies on productivity and sustainability.
  2. Bindraban, P. S., Dimkpa, C., Nagarajan, L., Roy, A., & Rabbinge, R. (2015). Revisiting fertilisers and fertilisation strategies for improved nutrient uptake by plants.* Biology and Fertility of Soils, 51(8), 897-911. This article reviews soil nutrient management and fertilizer optimization for better crop productivity.
  3. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. This review explores the role of machine learning and data analytics in enhancing agricultural practices, including nutrient management.
  4. Patil, S., Thakur, T., & Mehta, P. (2021). Agricultural decision support systems: A review of advances and future prospects.* International Journal of Information Technology, 13(1), 1-12. The paper examines decision support systems like DSSAT and Nutrient Expert and their applications in farming.
  5. Mittal, S., & Mehar, M. (2016). Socio-economic impact of mobile phones on Indian agriculture.* Indian Journal of Agricultural Economics, 65(4), 487-498. This study assesses the role of mobile-based advisory systems in delivering agricultural information to farmers.

In modern agriculture, maximizing crop yield while maintaining soil health has become a critical challenge. This fertilizer recommendation app leverages precision agriculture techniques to provide farmers with tailored fertilizer recommendations that align with specific crop needs, soil conditions, and climate data. The app integrates data from soil testing, crop requirements, and weather patterns to offer optimized fertilizer plans that minimize waste and environmental impact while boosting productivity. By guiding users on optimal nutrient application, the app aims to reduce fertilizer misuse, lower costs, and promote sustainable farming practices. This user-friendly, mobile- compatible app supports multiple crops, local languages, and delivers actionable insights to improve agricultural efficiency across various farming scales.

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