Road accidents caused by unexpected animal crossings are a major concern, especially during nighttime when visibility is poor. To address this issue, the proposed system introduces an advanced animal detection and alert framework designed to enhance road safety through continuous monitoring. The system employs a high-resolution night-vision camera to capture real-time footage of roadways. Deep learning models such as Convolutional Neural Networks (CNN) and YOLO are used to accurately identify animals even under low-light or foggy conditions. Once an animal is detected, the system immediately triggers alert signals to warn approaching vehicles, thereby reducing the chances of collision. This intelligent approach minimizes the need for human intervention and provides a scalable solution for highways and rural roads. The integration of AI-based vision technology with real-time detection ensures efficient performance and faster response. By combining automation, deep learning, and alert mechanisms, the proposed system aims to improve nighttime driving safety and prevent animal-related road accidents.
G. S, R. S, Sanjay A K et al.· 2026 4th International Confe...· 0 citations
Early diagnosis of tree leaf diseases is crucial for ensuring ecological stability, conserving biodiversity and sustainable agriculture productivity. Manual inspection and traditional image processing methods are often subjective, time-consuming, and susceptible to environmental changes like illumination variations, complex background and orientation of leaves in the images, which makes them difficult to work with. In response to the above drawbacks, the present work aims at proposing a novel, intelligent deep learning-based framework for automated detection of tree leaf diseases and supporting tree Agri-knowledge, namely PHYTOASSIST. The proposed approach is based on YOLO (You Only Look Once) convolutional neural network (CNN) for real-time object recognition of disease region on diseased leaf using high resolution image. To ensure robustness and generalisation capabilities to field scenarios, image pre-processing is used such as resize, normalisation and augmentation. The framework also includes a fertilizer and treatment recommendation module as well as an interactive agricultural chatbot that offers contextually relevant information for agricultural practice and disease prevention. Experimental results have proven the robustness of the accuracy (96.2%), precision (95.4%), recall (94.8%), and F1 score (95.1%) with an inference speed of less than 10 frames per second. The outcomes demonstrate the effectiveness of the design of the framework supporting the scalable agricultural disease monitoring and intelligent decision support in sustainable agricultural environments.
G. S, R. S, Sanjay A K et al.· 2026 4th International Confe...· 0 citations
