Automated Forest Health Monitoring and Optimal Harvest Prediction System for Sustainable Resource Management

Predicting the best times to harvest trees is crucial for managing forests sustainably, preventing illegal logging, and increasing financial gains. Manual surveys and static satellite imaging are the mainstays of traditional monitoring techniques, which have drawbacks such as inefficiency, geographic restrictions, and a delayed ability to identify ecological changes. These difficulties frequently lead to financial losses, ecological imbalances, and early or unlawful harvesting. This study suggests an AI-driven architecture [Figure.2], to automate forest health monitoring and improve harvest forecasts by combining high-resolution satellite imagery, geospatial data, and sophisticated machine learning algorithms. This study employs the CRISP-ML(Q) [Figure.1], methodology to develop a scalable framework for automated forest health monitoring and harvest prediction. By utilizing Google Earth Engine (GEE) APIs, the system collects multi-temporal and multi-spectral satellite imagery to enhance monitoring precision. Tree canopy segmentation is performed using polygon-based annotation techniques, while geospatial referencing of latitude-longitude coordinates ensures accurate mapping. The framework integrates Mask R-CNN [Figure.3], for tree detection and segmentation, estimating canopy diameters through pixel-to-meter ratio analysis. Additionally, LSTM networks are deployed to forecast tree growth patterns and determine optimal harvest times based on historical and real-time observations. To facilitate decision-making, an interactive web-based UI is designed to dynamically map tree locations, display predictive insights, and send real-time alerts to stakeholders. The dataset comprises high-resolution geotagged images annotated with precise growth metrics and enriched with vegetation indices such as NDVI and EVI, improving model reliability across diverse environments. By combining deep learning, geospatial analytics, and predictive modelling, this research establishes a data-driven, AI-powered framework for sustainable forestry management and biodiversity conservation.