Vegetation Analysis in Miyawaki Forest Using IOT Devices and Machine Learning
Deenadayalan M
Department Of Electronics and Communication Engineering
Panimalar Institute Of Technology chennai ,
india dayaland785@gmail.com
Muralidhara S
Department Of Electronics and Communication Engineering
Panimalar Institute Of Technology chennai
,india gamermd007@gmail.com
Harish S
Department Of Electronics and Communication Engineering
Panimalar Institute Of Technology chennai
,india harish28.surya@gmail.com
Dr.Sathyapriya S
Professor and head of the department
Department Of Electronics and Communication Engineering
Panimalar Institute Of Technology Chennai
,india priya.anbunathan@gmail.com
Dinesh M
Department Of Electronics and Communication Engineering
Panimalar Institute Of Technology chennai ,india
mdinesh0725@gmail.com
Dr.Jeyaramya V
Assistant Professor Department Of Electronics and Communication Engineering
Panimalar Institute Of Technology chennai
,india jeyaramyav@gmail.com
Abstract- 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.
Keywords: Remote Sensing for Vegetation, Data-Driven Decision Making,Soil-Adjusted Vegetation Index (SAVI), Modified Normalized Difference Water Index (MNDWI)
