AI-Enabled Reconfigurable Edge Device for Plant Health Assessment in Greenhouse Environment

Prabha Sundaravadivel, Rafael Reyes-Ramirez, Prajwal Mahesha Sethurani, Chase Brown, Shekhar Suman Borah, Shaker Kousik

Keywords: Handheld scanner, low-cost cameras, edge-AI, real-time inference, plant assessment

TL;DR: Edge-AI device for plant disease detection

Abstract: Timely detection of plant diseases in greenhouse environments is essential for minimizing crop loss, reducing chemical use, and supporting sustainable production. This paper presents an AI-enabled, reconfigurable edge device designed for leaf-level disease assessment using multimodal sensing and embedded intelligence. A 3D-printed handheld prototype was developed with a custom PCB integrating VIS/NIR spectral sensors, a thermal module, and an RGB camera for noninvasive measurement of physiological stress. A lightweight deep learning model combined with handcrafted spectral indices performs fully on-device analysis to detect early indicators such as abnormal thermal patterns, pigment loss, and reflectance changes linked to infections. The system is built on a low-power Raspberry Pi platform and supports model reconfiguration for rapid adaptation to different crops and disease types. Real-time inference is achieved through a segmentation-based pipeline deployed on both an Android app and an Ubuntu graphical interface. Validation trials demonstrated early disease detection with previously collected images and an average latency of 2.52 seconds on Android and 2.19 seconds on Ubuntu. We are currently calibrating our framework in the greenhouse environment for real-time assessments. A web dashboard provides live visualization of thermal overlays, spectral response, and health scores. By combining custom hardware, multimodal sensing, and on-device AI, the system offers a portable and scalable solution for early disease detection in greenhouse crops.

Submission Number: 15