MACHINE LEARNING AND REMOTE SENSING FOR AUTOMATED PLANT BIODIVERSITY MONITORING 
 https://doi.org/10.65725/RPSET/1/2/004

JOURNAL OF RESEARCH PERSPECTIVES IN MULTIDISCIPLINARY SCIENCE, EDUCATION AND TECHNOLOGY (RPSET)
Volume 1 Issue 2, April – June 2026

Abstract:
Monitoring plant biodiversity is essential for maintaining ecosystem balance, supporting environmental sustainability, and mitigating the impacts of climate change. Conventional biodiversity assessment methods rely heavily on field surveys, which are labor-intensive, time- consuming, and limited in spatial coverage. Recent advancements in remote sensing technologies and artificial intelligence (AI) provide scalable and automated solutions for large-scale ecological monitoring. This paper presents an AI-supported framework for plant biodiversity monitoring and ecosystem protection using remote sensing data and deep learning techniques. High- resolution multispectral satellite imagery is processed using convolutional neural networks (CNNs) to classify vegetation types, assess biodiversity patterns, and detect ecosystem disturbances such as deforestation and land degradation. Temporal analysis enables early identification of environmental changes, supporting proactive conservation efforts. Experimental evaluation demonstrates that the proposed deep learning-based approach achieves superior classification accuracy and robustness compared to traditional machine learning methods. The framework offers a reliable and efficient solution for sustainable ecosystem management and biodiversity conservation.

Authors: Dr..K.Sharmila, Dr.C.P.Prakash, Mrs.D.Sabareswari

Keywords: Plant Biodiversity Monitoring, Remote Sensing, Deep Learning, Convolutional Neural Networks, Ecosystem Protection, Artificial Intelligence.