Year: 2025
Type: article
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.
Author: Prof. Varsha Dange
Institution: Vishwakarma Institute of Technology
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4410160827