What question did this study set out to answer?

The aim is to develop a smart fertilizer recommendation system that improves crop yield and promotes sustainable agriculture.

May 15, 2026Open Access

Sustainable Fertilizer Usage Optimizer for Higher Yield

Key Points

The aim is to develop a smart fertilizer recommendation system that improves crop yield and promotes sustainable agriculture.
Developed an IoT-based system using an NPK sensor and ESP8266 microcontroller.
Implemented a Random Forest machine learning model to analyze soil data and recommend fertilizers.
Created a Flask web application for users to view soil parameters and fertilizer suggestions.
The system significantly improved crop yield by optimizing fertilizer usage based on soil conditions.
Real-time data analysis allowed for accurate fertilizer recommendations, reducing waste.
User engagement through the web application increased awareness of nutrient deficiencies and costs.

Abstract

This project presents an IoT-based smart fertilizer recommendation system for precision agriculture. The system uses an NPK sensor and ESP8266 microcontroller to collect real-time soil nutrient data and upload it to the ThingSpeak cloud platform. A Random Forest machine learning model analyzes the data and recommends suitable fertilizers based on soil conditions. A Flask web application allows users to view soil parameters, fertilizer recommendations, nutrient deficiency levels, and estimated costs. The system helps improve crop yield, reduce excessive fertilizer usage, and support sustainable farming practices.

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