This paper presents an AI-driven framework for real-time reverberation control in dynamic environments. The system integrates parametric modeling in Grasshopper, Pachyderm acoustic simulation, and machine learning to create a closed-loop controller. A CNN estimates reverberation time from audio signals, while a reinforcement learning agent dynamically adjusts panel absorption coefficients to maintain optimal acoustics. Evaluation showed the system should be able to maintain T60 within 0.15 s of the target under varying occupancy and source positions, outperforming static treatments and enabling self-regulating acoustic environments for improved auditory experiences.

Leaf based diseases in tomatoes such as early blight, late blight, and septoria leaf spot, pose a significant threat to global food security and have substantial economic impacts. Early detection of these diseases is crucial for improving crop yields. This paper explores the use of vision-language models (VLMs) for detecting tomato leaf diseases by fine-tuning a pre-trained model on a large dataset of tomato leaf images with corresponding disease annotations. This approach enhances disease detection accuracy and enables multi-modal learning, real-time monitoring, and automated diagnosis, offering promising applications in precision farming and food production.