AgroSense: AI-Driven Smart Farming Platform

Abstract

Our country is predominantly agro-based, with the majority of farmers relying on agriculture for their livelihoods. However, they face critical challenges such as unpredictable climate, inefficient resource utilization, and declining soil health. These factors result in suboptimal crop yields and a growing need for informed, data-driven decisions in farming practices. This system integrates IoT sensors like ESP8266 to monitor real-time environmental factors, such as temperature, humidity, soil moisture, and vibration. Using a web-based platform built with Python and Streamlit, farmers can easily access a user-friendly interface for visualizing and analyzing data. The system also leverages machine learning models for disease prediction and crop yield optimization, utilizing algorithms such as Logistic Regression and Random Forest Classifier. The platform processes data using libraries like Scikit Learn, Matplotlib, NumPy, and Pandas for data manipulation, model training, and visualization. This system empowers farmers with actionable insights to optimize resource use, improve crop health, and make informed decisions, all while promoting sustainable farming practices. This affordable and scalable solution aims to revolutionize traditional agriculture, making it more precise, efficient, and environmentally friendly.

Introduction

Agriculture is a crucial part of the economy, supporting many livelihoods, but faces challenges like unpredictable climate, inefficient resource use, and soil degradation, leading to lower crop yields and food security risks. To address this, the integration of IoT and machine learning offers a path to smarter, more sustainable farming. This research develops a cost-effective, scalable system using IoT sensors (ESP8266) to monitor environmental factors in real-time and applies machine learning models (Logistic Regression and Random Forest) to predict crop diseases and optimize yields. A web-based platform built with Python and Streamlit visualizes data and delivers actionable insights to farmers, helping improve resource management and crop health.

The literature review highlights similar IoT and AI applications in agriculture, including sensor networks, cloud computing, mobile apps, and data analytics, all aimed at increasing productivity and sustainability. Challenges like data management, cost, and adoption remain central.

The system architecture integrates sensors, cloud processing, machine learning, and a user interface to provide real-time monitoring and predictive analytics. The methodology involves identifying farmer needs, deploying sensors, collecting and preprocessing data, training machine learning models, developing a user-friendly platform, and implementing decision support with alerts. Testing in simulated farm conditions validates sensor accuracy and model performance. The system is designed for affordability, scalability, sustainability, and ease of adoption, aiming to empower farmers with precise data-driven tools to improve agricultural outcomes.

Conclusion

In all, the IoT-based smart farming system represents a significant advancement in the integration of technology into agriculture, offering farmers the tools necessary to make data-driven decisions that optimize resource usage, improve crop health, and enhance overall productivity. By leveraging IoT sensors for real-time monitoring of critical environmental factors, such as temperature, humidity, soil moisture, and vibration, the system provides farmers with accurate, actionable insights to manage irrigation, fertilization, and pest control effectively. The incorporation of machine learning models, including Logistic Regression for disease prediction and Random Forest for crop yield optimization, has proven effective in addressing key challenges in farming, offering reliable predictions and improving farm management practices. The system\'s user-friendly platform ensures that even farmers with limited technical expertise can access and interpret the data with ease, contributing to the technology’s potential for widespread adoption. Furthermore, the system’s scalability allows it to handle an expanding number of sensors and data points, supporting the growing demands of modern agriculture. Despite its successes, the system does have certain limitations, such as sensor calibration under extreme weather conditions and the dependence on a stable internet connection for optimal functionality. These challenges present opportunities for further research and development, including the introduction of adaptive calibration methods, enhanced machine learning models, and offline capabilities to broaden the system\'s applicability in remote and underserved areas. Overall, the system has the potential to revolutionize traditional farming practices, making agriculture more efficient, sustainable, and capable of meeting the challenges of a rapidly changing environment. With continued improvements and the integration of advanced analytics, such as satellite imagery for crop health monitoring, this smart farming system could serve as a transformative solution in the global effort to create more sustainable food production systems.

References

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Copyright

Copyright © 2025 Prof. Varsha Dange, Sandesh Kadam, Siddhi Sangwai, Shreyas Ruikar, Rijul Sidanale, Siddharth Sadake. 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.