Our project utilizes deep learning to detect diseases in citrus fruits swiftly and accurately. With a curated dataset and optimized CNN models, we empower farmers with a user-friendly tool for early disease detection, precision agriculture, and informed decision-making. Contributions and collaborations are welcome for further advancements.
Classroom Monitor is a system designed to track, manage, and analyze classroom activities in real time, helping educators maintain discipline, engagement, and efficiency.
This real-time system uses YOLOv8 and Deep SORT to detect and track objects from a webcam or video file. It draws bounding boxes, class labels, and unique tracking IDs on live video. The app supports input switching, runs smoothly on CPU, and is optimized for accuracy and performance.
A project for real-time fish species detection using YOLOv5 in dynamic aquatic environments. Aims to support ecological monitoring and sustainable marine resource management. Utilizes YOLOv5, PyTorch, and Roboflow for accurate detection and behavior analysis.
AI-powered Brain MRI Tumor Detection system using InceptionV3, U-Net, and Streamlit.
Detects AI-generated audio using Shannon entropy features + Random Forest. 100% blind test accuracy. No GPU required. Trained on LibriSpeech vs Kokoro TTS.
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