AniMonitor: A Low-Cost Multispectral Imaging Camera for Early Disease Detection and Management in Rice Crops
Year: 2025
Type: dissertation
Abstract: AniMonitor is a low-cost multispectral imaging system using Raspberry Pi and bandpass-filtered cameras to detect early signs of rice crop disease. It combines NDVI image analysis, machine learning, and a web platform for real-time detection, data capture, and monitoring. A fine-tuned VGG16 convolutional neural network trained on 256 × 256 NDVI composites runs on a Python/Flask back-end and achieves 86.7 % overall accuracy in field trials conducted at PhilRice-UPLB. AniMonitor offers a scalable, farmer-friendly tool that enables early disease detection and supports precision-agriculture initiatives in resource-constrained environments.
Authors: Quitalig, Diane Lalaine, Nombrado, Billy, Alvarez, Neil Paul, Montances, Mark
Institution: De La Salle University – Dasmariñas
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W6893255153
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