Fertilizer Prediction Using Machine Learning

Abstract

Fertilizer application is an important aspect of sustainable agriculture, having a direct influence on crop yield and soil health. Yet, if not used correctly, fertilizers can have negative environmental impacts and reduced productivity. This study seeks to create a machine learning model for precise fertilizer prediction, utilizing a Random Forest algorithm. The model takes into account key agricultural parameters, such as temperature, humidity, soil moisture, soil type, crop type, and the levels of nitrogen, potassium, and phosphorus, as well as pH levels. Utilizing a strong dataset, our Random Forest-based model scored an accuracy of 95%, proving its potential to assist farmers in choosing the best fertilizer for a given condition. This not only optimizes fertilizer usage but also encourages sustainable agriculture by reducing the environmental footprint. The findings of this research highlight the importance of incorporating machine learning in precision agriculture to boost productivity and ecological balance.

Introduction

1. Introduction

The agricultural sector is critical for a country’s economy and global food security. However, traditional farming methods are often based on subjective experience and lack data-driven precision, leading to inefficiencies. To overcome these issues, modern agriculture must adopt technology-enabled precision farming using tools like machine learning (ML), big data, and image processing.

2. Technological Solutions

Emerging technologies can improve crop selection, pest control, and fertilization.

• Machine Learning helps by analyzing past data to offer evidence-based, personalized farming recommendations.

• Image Processing aids in disease detection and assessing soil and crop health.

• Despite progress, most existing systems still do not consider multiple critical variables (e.g., soil type, weather, water availability), leading to suboptimal results.

3. Proposed Solution

A Random Forest-based fertilizer prediction system was developed.

• Inputs: Soil type, crop type, temperature, humidity, and nutrient levels.

• Output: Fertilizer recommendations with 95% accuracy.

• Benefits: Boosts efficiency, sustainability, and overcomes challenges in traditional farming.

4. Literature Survey (Highlights of Related Work)

• Various studies applied ML models like Gradient Boosting, Random Forest, and ensemble models for crop and fertilizer prediction, achieving up to 99% accuracy.

• Tools using IoT, satellite data, and decision-support systems were explored for smarter farming.

• Challenges like soil diversity, ecological variability, and limited fertilizer use (especially in Sub-Saharan Africa) were discussed.

• Emphasis was placed on automation, robotics, and AI integration to improve productivity and reduce labor demands.

5. System Design

• Architecture & Flow: Outlines how input parameters flow through the system to produce recommendations.

• Mathematical Derivation:

  • Random Forest Voting Mechanism explained.

  • Accuracy Formula derived based on TP, TN, FP, FN metrics.

6. Methodology

• Tools Used: Python (3.6.2), Flask, NumPy, Pandas, Scikit-learn.

• Modules:

  • Dataset Upload (1500 records with 7 parameters).

  • Feature Extraction, Model Training, and Performance Evaluation.

  • Dynamic Input Testing with real-time user data.

• Data Collection & Preparation:

  • Data gathered from credible sources.

  • Preprocessing included exploration, handling outliers, and dealing with missing values.

Conclusion

In summary, the system created in this project effectively applies machine learning algorithms, Random Forest in this case, to make precision and efficient fertilizer recommendations depending on various environmental and soil parameters. The capacity of the system to use historical data in conjunction with its simple-to-use interface created with Python and Flask provides a realistic solution for farmers to maximize their crop harvests. Attaining an accuracy rate of 95% showcases the capabilities of machine learning to improve agricultural activities. The project not only identifies the significance of data-driven decision-making but also paves the way for subsequent innovations in smart agriculture. With additional enhancements, including the addition of real-time data and extended parameter sets, this system can be a crucial tool for sustainable agriculture, promoting enhanced crop yield with reduced environmental consequence from fertilizer usage.

References

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Copyright

Copyright © 2025 Prof. Bhagwan Thorat, Ishan Ranadive, Nitesh Rajpurohit, Yashashree Mehare, Rajroushan Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.