ADVANCING SOIL NUTRIENT MANAGEMENT IN AGRICULTURE - WITH INTEGRATING MACHINE LEARNING AND FUZZY LOGIC APPROACHES

Aug 3, 2026

Agriculture faces multifaceted challenges affecting food security, sustainability, and economic growth. Soil forms the foundation for food production, yet indiscriminate fertilizer use drives pollution and degradation, making integrated nutrient management essential. This paper studies how fuzzy logic supports soil nutrient categorization and decision-making under uncertainty, while machine learning models predict soil fertility and crop yield. The work evaluates multiple classifiers and proposes recommendations of suitable crops and fertilizers based on soil characteristics.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Abstract

Problems in Agriculture

Climate-related stresses and resource constraints

Soil degradation and biodiversity loss

Rural poverty and food insecurity

Soil Nutrient Management

Importance of soil health

Integrated nutrient management (INM)

Soil health card and implementation challenges

Fuzzy Logic and Machine Learning Integration

Nutrient categorization and decision support

Predictive modeling for fertility and yield

Recommendation of crops and fertilizers

19(2): 81-87, 2024 [www.thebioscan.com](www.thebioscan.com) ADVANCING SOIL NUTRIENT MANAGEMENT IN AGRICULTURE WITH INTEGRATING MACHINE LEARNING AND FUZZY LOGIC APPROACHES GIGI ANNEE MATHEW1, VARSHA JOTWANI2 AND A. K. SINGH*3 1Research Scholar, Department of Computer Science & IT, Rabindranath Tagore University,Bhopal-464993, India, E-mail: [gigiannee@gmail.com](gigiannee@gmail.com) 2HoD (CS & IT), Department of Computer Science & IT, Rabindranath Tagore University,Bhopal-464993, India, [E-mail: varsha.jotwani@aisectuniversity.ac.in](E-mail: varsha.jotwani@aisectuniversity.ac.in) 3Scientist, Krishi Vigyan Kendra, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur-482004, M.P., India, E-mail: [singhak123@rediffmail.com](singhak123@rediffmail.com), ORCID ID: 0000-0002-7644-5802 Corresponding Author Contact Number: +919424638238 DOI: [https://doi.org/10.63001/tbs.2024.v19.i02.pp81-87](https://doi.org/10.63001/tbs.2024.v19.i02.pp81-87) KEYWORDS Soil nutrient management, machine learning, fuzzy logic, classification, categorization, prediction, recommendation system Received on: 05-04-2024 Accepted on: 02-09-2024 Corresponding author ABSTRACT Agriculture faces multifaceted challenges that affect food security, sustainability, and economic growth. Soils serve as the foundation for food production. However, indiscriminate use of fertilizers has led to soil pollution and degradation, necessitating integrated nutrient management practices. Machine learning (ML) emerges as a transformative technology in agriculture, offering solutions across various domains. Fuzzy logic, with its ability to handle uncertainty and imprecision, complements machine learning in agricultural decision support systems. This paper explores the utilization of fuzzy logic for soil nutrient categorization and decision-making, along with the analysis of machine learning models for predicting soil fertility and crop yield and also examines the recommendation of suitable crops and fertilizers based on soil characteristics. These models leverage diverse algorithms such as K-Nearest Neighbours, Random Forest, Naive Bayes, Support Vector Machine, Decision Trees and ensemble classifiers to offer accurate predictions and recommendations. The integration of ML and fuzzy logic in agriculture represents a potential approach to tackling agricultural challenges, advancing sustainable soil management practices, and elevating crop productivity. Problems in Agriculture Global and local agriculture confronts a number of formidable obstacles that have an effect on food security, sustainability, and economic growth. Severe conditions including heat waves, floods, and droughts brought on by climate change reduce animal production and yields of crops. (Lobell et al., 2011) . Water scarcity intensifies agricultural problems, particularly in arid and semi-arid regions. As demand for water increases, its resources decline due to pollution, overuse, and the effects of climate change (FAO, 2020) . Soil degradation presents another concern, with erosion, salinization, acidification, and loss of fertility posing threats to agricultural productivity and sustainability (Lal, 2015) . Pests and diseases continually affect crops and livestock, causing substantial losses in yield and economic returns, if left unmanaged (Savary et al., 2019) . Moreover, the loss of biodiversity due to agricultural intensification, monoculture practices, and habitat destruction undermines ecosystem services critical for agricultural resilience and long-term sustainability (Tscharntke et al., 2012) . Rural poverty and food insecurity are a persistent challenge, particularly among small farmers in developing nations, who often lack access to markets, credit, technology, and resources necessary for sustainable agricultural practices (FAO, 2021) . Food wastage and losses in the agricultural supply chain further aggravate global hunger, economic losses, and environmental degradation,

FAQs

Why is integrated nutrient management important for agriculture?

Indiscriminate fertilizer use degrades soil quality and contributes to pollution. Integrated nutrient management supports healthy soil, productivity, and more sustainable fertilizer inputs.

How do fuzzy logic and machine learning complement each other in soil decision support?

Fuzzy logic handles uncertainty and imprecision in agricultural judgments. Machine learning provides predictive capabilities for soil fertility, crop yield, and recommendation tasks.

Which machine learning models are used for prediction and recommendations?

The paper discusses using algorithms such as K-Nearest Neighbours, Random Forest, Naive Bayes, Support Vector Machine, Decision Trees, and ensemble classifiers.

Aug 3, 2026

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