Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3946 2019 2019 Assistant Professor, Department of Artificial Intelligence and Data Science, St. Joseph's Institute of Technology, OMR, Chennai, India Abhilash Abhilash Associate Professor, Department of Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, India Manu Gupta Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad 500043, Telangana, India J. Sirisha Devi Assistant Professor, Department of Artificial intelligence and Data Science, Panimalar Engineering College, Chennai, Tamilnadu, India A Babisha 5Assistant Professor, Department of Computer Science and Engineering, Aditya University, Surampalem, India D. Venkata Ravi Kumar Professor, Department of CSE, Mohan Babu University, Tirupati, India B. Rama Subba Reddy With direct implications for the regional climate, biogeochemistry, hydrology, and biodiversity, land cover change has been identified as one of the top priorities for the development of sustainable management plans. Among the primary causes of global warming are deforestation and forest fragmentation, which have profound effects on biodiversity preservation and ecosystem functioning. Machine learning techniques, like those employed in computer vision, have become widely used, making it possible to segment satellite images semantically to distinguish between areas that are forested and those that are not. This study presents a novel method for segmenting and classifying UAV images to detect deforestation using machine-learning models. In this case, noise reduction as well as normalisation is applied to input, which consists of UAV-based forest region photos. Semantic U-convolutional regressive neural network combined with deep radial quantile temporal neural network was then used to segment and classify this image. The suggested model's simulation analysis is assessed based on several metrics, including F-1 score, normalized coefficient ratio, average precision, AUC, and detection accuracy. proposed method yielded 97% detection  accuracy, 93% normalized coefficient ratio, 91% AUC, F-1 score of 94% and 95% AVERAGE PRECISION. 2026 2026 218 226 10.54216/JISIoT.180116 https://www.americaspg.com/articleinfo/18/show/3946