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gadamvivek01/PrecisionFarming

sourceHugging Faceupdated 7mo agoView on Hugging Face

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App README

SmartFarming — AI-Powered Precision Agriculture

Smart Farming is an IoT and ML-based system that helps farmers make data-driven decisions on crop selection and fertilizer usage. This project originated from a startup focused on precision agriculture using LoRaWAN sensors and machine learning.

Features

—Crop Recommendation — Input soil NPK values, temperature, humidity, pH, and rainfall. Six ML models (Random Forest, XGBoost, SVM, Decision Tree, Naive Bayes, Logistic Regression) each predict the best crop. A majority vote gives the final recommendation.
—Fertilizer Recommendation — Input your crop type and current soil NPK readings. The system compares against ideal values and recommends corrective action.

ML Models

Model	Algorithm
Random Forest	Ensemble (bagging)
XGBoost	Ensemble (boosting)
SVM	Support Vector Machine
Decision Tree	Tree-based
Naive Bayes	Probabilistic
Logistic Regression	Linear

All models trained on the Crop Recommendation Dataset (2200 samples, 22 crops).

Tech Stack

—Backend: Python, Flask
—ML: scikit-learn, XGBoost
—Frontend: HTML, CSS, JavaScript
—Deployment: Docker on Hugging Face Spaces

Background

This project is part of a larger Precision Farming system that integrates:

—LoRaWAN IoT sensors (NPK, pH, moisture, humidity)
—ThingsMatee cloud gateway
—Automated SMS alerts via Twilio
—Scheduled ML inference pipeline

See PrecisionFarming for the full IoT automation codebase.