Ferticast - Data-Driven Fertilizer Optimization System Embedded with Rainfall Prediction

Abstract

This paper introduces a machine learning based fertilizer recommendation system created to help farmers make better and more informed decisions about nutrient application. The system studies key factors such as soil condition, rainfall behavior, and local weather to estimate the right amount of nutrients required for healthy crop growth. It uses Random Forest Regression and time-based analysis to understand patterns and provide reliable recommendations. By including rainfall prediction, the system also helps reduce nutrient loss caused by heavy rain and improves the overall efficiency of fertilizer use. The platform is available through a simple web interface where farmers can enter their crop type and location to receive accurate and timely suggestions. The proposed approach supports modern, data driven farming and promotes the responsible use of fertilizers for sustainable agriculture.

Introduction

The text reviews the evolution of fertilizer management from traditional, experience-based approaches to modern, machine learning–driven systems aimed at improving sustainable agriculture. Traditional fertilizer recommendations relied on soil testing and expert judgment but were limited by scalability, environmental variability, and an inability to capture complex nutrient interactions. Variations in climate, rainfall, soil type, and crop rotation further complicate accurate nutrient estimation and often reduce fertilizer efficiency.

Recent research highlights machine learning as an effective solution for handling these complexities. Techniques such as Random Forests, Support Vector Machines, neural networks, deep learning, and ensemble models have been widely applied to predict crop yield and nutrient requirements using soil, weather, and crop data. These data-driven approaches generally outperform traditional methods in accuracy, adaptability, and resilience to environmental variation. Hybrid systems that combine machine learning with decision support tools, cloud platforms, and mobile or IoT-based interfaces have further improved real-time, field-level fertilizer guidance, though challenges remain related to data quality, interpretability, and deployment in resource-limited settings.

Building on this literature, the proposed system, FertiCast, is a smart fertilizer advisory tool that dynamically adjusts NPK recommendations based on rainfall and weather conditions. Using Random Forest regression, the system predicts nitrogen, phosphorus, and potassium requirements while accounting for climate-driven nutrient losses. A rainfall decision module prevents or delays fertilizer application during heavy rainfall to reduce nutrient leaching. Delivered through a web-based interface, FertiCast integrates user input, weather intelligence, machine learning predictions, and rainfall rules to provide adaptive, accessible, and environmentally sustainable fertilizer recommendations for farmers.

Conclusion

This research demonstrates that fertilizer recommendation should not be treated as a fixed guideline but must adapt to weather fluctuations, especially rainfall, which has a direct impact on nutrient retention and crop response. Continuous studies show that nutrient losses intensify during heavy or prolonged precipitation, leading to reduced fertilizer efficiency and unnecessary cost to farmers [1], [3]. Such conditions, if ignored, lead to wastage of nitrogen and potassium due to leaching and runoff, affecting both yield and soil health. FertiCast addresses this challenge by combining ensemble machine learning with climate-responsive advisory logic. The use of Random Forest improves prediction stability and makes nutrient estimation more reliable under varying field conditions [8], [17], [21]. This ensures that fertilizer values are not only crop-specific but also location-dependent and sensitive to climatic variations. By preventing fertilizer application during unsafe rainfall periods, the system improves nutrient use efficiency and reduces environmental losses. Digital platforms and cloud-based advisory systems also prove beneficial in disseminating such intelligent agricultural decision support without requiring expensive laboratory testing or sensor-based infrastructure [13], [16]. As a result, FertiCast acts as a practical and accessible tool that supports real-time nutrient planning, sustainable fertilizer use, and informed agricultural decision-making. Overall, the integration of rainfall forecasting, machine learning prediction and web-based advisory converts FertiCast into a proactive decision system rather than a static calculator. It not only suggests how much fertilizer to use but also determines when it is economically and environmentally appropriate to apply it, promoting efficient and climate-aware fertilizer management in modern agriculture.

References

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Copyright

Copyright © 2025 Hemant Kushwaha, Swechchha Maurya, Shreya Jaiswal, Ms. Jyoti Gaur. 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.