Revolutionizing Fertilization Strategies with Machine Learning-Driven Nutrient Prediction

AUTHOR(S)

Mrs. J. Porkodi, Dr. B. Karunai Selvi, Mr. A. Nagavaratharajan

DOI: https://doi.org/10.46647/ijetms.2023.v07i05.021

ABSTRACT
In the pursuit of sustainable agriculture, the effective management of fertilization strategies stands as a critical imperative. This abstract explores the transformative potential encapsulated within "Revolutionizing Fertilization Strategies with Machine Learning-Driven Nutrient Prediction." Traditional agricultural practices often grapple with imprecise fertilization, leading to inefficiencies, overuse of resources, and environmental ramifications. This study introduces an innovative approach that integrates advanced Machine Learning (ML) techniques with agronomic insights to accurately predict plant nutrient requirements. By harnessing comprehensive datasets encompassing soil properties, crop categorizations, and historical growth data, an intricate ML model is formulated. Employing sophisticated algorithms such as Random Forest, XG Boost Classifier the model uncovers intricate interdependencies shaping nutrient absorption. Through an iterative process of training, validation, and optimization, the model attains the capability to anticipate nuanced nutrient demands across diverse growth stages and crop typologies. By supplying real-time, data-informed intelligence on nutrient requirements, farmers are empowered to tailor fertilization approaches with precision, curbing resource wastage and diminishing nutrient runoff. Additionally, this ML-Centered methodology aligns seamlessly with the ambitions of sustainable agriculture, channelling efforts toward resource efficiency and environmental stewardship.
Page No: 184 - 187

References:

Dorijan Radocaj, "The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture," Remote Sensing for Water Resources Assessment in Agriculture, 2022.

Andavar Suruliandi, Kharla Andreina Segovia-Bravo, Soosaimarian Peter Raja, "Predicting the suitable fertilizer for crop based on soil and environmental factors using various feature selection techniques with classifiers," Expert Systems, 13 July 2022.

Alicia Hernandez-Mora , Walter W. Wenzel , "Improving the prediction of fertilizer phosphorus availability to plants with simple, but non-standardized extraction techniques," Science of The Total Environment, September 30 ,2021.

John E. Sawyer, Javed Iqbal, Aaron M. Sassman, Renuka Mathur, "Role of sulfur mineralization and fertilizer source in corn and soybean production systems," SOIL FERTILITY & PLANT NUTRITION, 31 March 2022.

Michela Battisti, Barbara Moretti, Dario Sacco, "Soil Olsen P response to different phosphorus fertilization strategies in long-term experiments in NW Italy," Soil Use and ManagementVolume 38, Issue 1 p. 549-563,2021

Laasya Kanuru; Amit Kumar Tyagi; Aswathy S U, “Prediction of Pesticides and Fertilizers using Machine Learning and Internet of Things”, IEEE 2021 International Conference on Computer Communication and Informatics (ICCCI -2021), Jan. 27-29, 2021

Jeevaganesh R; Harish D; Priya B ,”A Machine Learning-based Approach for Crop Yield Prediction and Fertilizer Recommendation”, 2022 IEEE 6th International Conference on Trends in Electronics and Informatics (ICOEI), 28-30 April 2022.

S. Bhanumathi; M. Vineeth; N. Rohit ,” Crop Yield Prediction and Efficient use of Fertilizers”, 2019 International Conference on Communication and Signal Processing (ICCSP) ,25 April 2019.

K P K Devan; B Swetha; P Uma Sruthi; S Varshini ,” Crop Yield Prediction and Fertilizer Recommendation System Using Hybrid Machine Learning Algorithms”, 2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT), 31 May 2023.

G. Elizabeth Rani; Ede Venkatesh; Karnam Balaji,” An automated prediction of crop and fertilizer disease using Convolutional Neural Networks (CNN)” 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE),18 July 2022.

Hongjian Zhang; Chunbao Xu ,” Fertilizer Strength Prediction Model Based on Shape Characteristics” , IEEE Access ( Volume: 9), 23 March 2021.

K Monika; Balakrishnan Ramprakash; Sankayya Muthuramalingam; K Mirdula”Crop Fertilizer Prediction using Regression analysis and Machine Learning algorithms”, 2022

Krutika Hampannavar; Vijay Bhajantri; Shashikumar. G. Totad ,” Prediction of Crop Fertilizer Consumption”, 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) ,25 April 2019.

O. Rama Devi; P. Naga Lakshmi; S.Naga Babu ,” Fertilizer Forecasting using Machine Learning”, 2023 International Conference on Inventive Computation Technologies (ICICT), 01 June 2023.

Sai Yasehwanth Chaganti; Prajwal Ainapur ,” Prediction Based Smart Farming”, 2019 2nd International Conference of Computer and Informatics Engineering (IC2IE), 27 December 2019.

How to Cite This Article:
Mrs. J. Porkodi, Dr. B. Karunai Selvi, Mr. A. Nagavaratharajan . Revolutionizing Fertilization Strategies with Machine Learning-Driven Nutrient Prediction . ijetms;7(5):184-187. DOI: 10.46647/ijetms.2023.v07i05.021