International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
SUSTAINABLE FERTILIZER USAGE OPTIMIZER FOR HIGHER YIELD Mr. V.Murugan1 , Ujvala.M2, RAVI.P3, Noorein Fatima4, Nithin.N5 1Assistant Professor, Department of IT, TKR College of Engineering and Technology, Telangana, India 2,3,4,5B.Tech Students, Department of IT, TKR College of Engineering and Technology, Telangana, India
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Abstract - Agricultural productivity is highly influenced by
can analyze soil parameters such as Nitrogen (N), Phosphorus (P), Potassium (K), pH, moisture, and climatic conditions such as rainfall and temperature to recommend suitable crops, predict yield, and suggest fertilizer requirements [1]. Studies show that ensemble learning methods such as Gradient Boosting, Random Forest, and XGBoost provide better performance compared to traditional ML models due to their ability to handle nonlinear agricultural datasets and improve prediction accuracy [2].
soil nutrients, climatic conditions, and farming practices such as fertilizer usage and crop rotation. However, many farmers still rely on traditional knowledge and intuition for crop selection and fertilizer application, which often leads to reduced yield, higher input costs, and long-term soil degradation. To address this issue, this paper proposes a Sustainable Fertilizer Usage Optimizer for Higher Yield, an intelligent web-based agriculture recommendation system that uses machine learning and ensemble learning techniques. The system predicts soil characteristics, recommends the most suitable crop, estimates expected yield, and suggests the optimal fertilizer type and quantity based on soil parameters such as Nitrogen (N), Phosphorus (P), Potassium (K), pH, moisture, and weather conditions including temperature and rainfall. Advanced models such as LightGBM, XGBoost, AdaBoost, Extra Trees, and Gradient Boosting are utilized, and their performance is enhanced using ensemble strategies like bagging, boosting, and stacking. Cross-validation and hyperparameter tuning are applied to improve accuracy and reduce overfitting. The proposed system supports sustainable farming by minimizing excessive fertilizer usage, improving crop yield, and enabling farmers to make data-driven decisions. This approach enhances productivity, reduces environmental impact, and increases long-term profitability for farmers.
Fertilizer usage is another major concern in modern agriculture. Excessive and improper fertilizer application results in nutrient imbalance, groundwater pollution, reduced soil quality, and environmental damage. Sustainable fertilizer recommendation systems can help in reducing these issues by providing optimal fertilizer type and quantity based on soil nutrient deficiency and crop requirement [3]. Similarly, yield prediction plays a crucial role in planning harvesting strategies, supply chain management, and market decision-making for farmers. Yield prediction using historical yield records, soil health parameters, and weather conditions has been widely researched and is considered an effective approach for improving profitability [4]. This project proposes a Sustainable Fertilizer Usage Optimizer for Higher Yield, a web-based intelligent recommendation system that integrates multiple machine learning algorithms such as LightGBM, XGBoost, AdaBoost, Extra Trees, and Gradient Boosting. The system supports four major functions: Soil Prediction, Crop Recommendation, Fertilizer Suggestion, and Yield Prediction. Ensemble learning techniques like bagging, boosting, and stacking are applied along with cross-validation and hyperparameter tuning to enhance model accuracy and generalization. The proposed system provides farmers with reliable, real-time recommendations to increase yield, reduce input cost, and promote sustainable agricultural practices.
KEYWORDS:PrecisionAgriculture,Crop ecommendation, Fertilizer Optimization, Yield Prediction, Soil Nutrient Analysis
1. INTRODUCTION Agriculture plays a vital role in ensuring food security and supporting the economy of developing countries such as India. A major portion of the population depends on farming as their primary source of income. However, agricultural productivity is influenced by multiple factors such as soil nutrient levels, climatic variations, irrigation availability, crop rotation, and fertilizer management. In many cases, farmers rely on traditional knowledge and experience for selecting crops and applying fertilizers. Although these practices are useful, they often fail to provide accurate decisions under changing environmental conditions and lead to low yield, increased production cost, and soil fertility degradation. Recent advancements in precision agriculture and datadriven farming have enabled the use of machine learning (ML) techniques to improve crop productivity. ML models
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Impact Factor value: 8.315
1.1 Motivation The motivation behind this work is to support farmers by providing an intelligent decision-making system for crop selection and fertilizer optimization. Farmers often face uncertainty due to unpredictable climate, soil nutrient imbalance, and lack of scientific recommendations. By integrating soil and weather parameters with machine learning models, this system aims to improve productivity, reduce fertilizer misuse, and enhance sustainability.
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