Vegetation Analysis in Miyawaki Forest Using IOT Devices and Machine Learning

The Smart Vegetation Analysis and Crop Recommendation System integrates NPK soil sensors, machine learning (ML) models, and Sentinel-2 satellite data to analyze soil health and suggest suitable crops for the Miyawaki forest in Chennai. Real-time soil nutrient data (Nitrogen, Phosphorus, and Potassium) is collected using Arduino-connected sensors and combined with vegetation indices such as NDVI, SAVI, and MNDWI from satellite imagery to assess plant health. An ML model processes these inputs to recommend optimal crops based on local soil conditions, climate factors, and vegetation characteristics. This approach enhances precision farming and sustainable forest management by providing data-driven insights for monitoring ecosystem health. The system aids in real-time soil analysis, efficient resource utilization, and better decision-making for afforestation and agricultural planning.