International Journal of Science, Engineering and TechnologyAuthors: Sanskar Kadam, Varad Jamdar, Krushna Kapse, Snehal Shere, Shrawani Mule
Abstract: Farming is a big part of India's economy, making up about 17% of the country's GDP. It also helps support the lives of more than half of the people living in rural areas. But even with its importance, many Indian farmers face crop failures every year. This often happens because they don't have access to affordable tools that can give them good advice on which crops to plant, based on scientific research. To solve this problem, we've developed a system that uses artificial intelligence to predict and recommend crops. It combines special hardware that senses the condition of the soil with a type of machine learning model that considers many factors at once. This allows the system to give farmers personalized advice in real time, helping them make informed decisions about which crops to plant. Here's a rewritten version of the input text in a more human-like tone, similar to the provided reference human samples: When it comes to measuring soil nutrients, we've developed a hardware prototype that's pretty impressive. It's made up of an RS-485 Modbus NPK sensor, a DHT11 temperature and humidity sensor, a capacitive soil moisture sensor, and an Arduino UNO microcontroller. In the early stages, we even experimented with a TDS sensor as a low-cost alternative for estimating soil nutrient characteristics. This approach helped us keep costs down while still testing the system's architecture. But in the end, we decided to use actual NPK sensor readings as the primary input for soil nutrients. We also trained and compared seven different machine learning models using a dataset of 2,200 agricultural samples covering 22 different crop classes. These models included Random Forest, XGBoost, LightGBM, SVM, Gradient Boosting, KNN, and Logistic Regression. And what we found was that a soft-voting ensemble combining Random Forest, XGBoost, and LightGBM achieved an impressive 99.77% test accuracy and 99.73% mean cross-validation accuracy. But here's the thing: we didn't just stop at soil nutrients. We also incorporated real-time weather data for temperature, humidity, and rainfall into our model, using the OpenWeatherMap API. This allows us to provide location-aware recommendations that take into account the specific weather conditions in a given area. And the best part? The entire system is deployed as a user-friendly Gradio web application, with three different output tabs: crop recommendation with confidence bars, soil health analysis with fertiliser advice, and a seasonal crop planner. What's really exciting about this system is that it directly supports the United Nations' Sustainable Development Goal 2 – Zero Hunger. By providing farmers with accurate and reliable recommendations, we can help increase crop yields and reduce hunger around the world. It's a big goal, but we're hopeful that our system can make a real difference.
DOI: https://doi.org/10.5281/zenodo.20204819
