SaBaTech: A Banana Fruit Pest and Disease Detection Web Application
Year: 2025
Type: article
Abstract: Many farmers in the Philippines make a living from the banana sector, which is essential to the country’s economy. Diseases and pests still have an impact on crop quality and output, though. In order to identify banana pests and diseases, this paper introduces SaBaTech, a web-based, real-time detection and classification tool that makes use of image processing and machine learning. The system, which was created using the Spiral Model, uses TensorFlow and Keras for deep learning and an ESP32-CAM for real-time picture capturing. For well-informed decision-making, it offers data visualization, SMS alerts, and report generating. SaBaTech received the highest “Functional Suitability” score (3.72), demonstrating its correctness, dependability, and efficiency, according to ISO/IEC 25010 testing. Its impact and usefulness are supported by input from agricultural practitioners, students, staff, and IT specialists. SaBaTech is a potential tool for precise agriculture; future developments include adding support for additional crops, increasing hardware-software compatibility, improving real-time processing, and lowering connectivity problems at distant fields.
Authors: M. D. Gerance, K. Sta Rufina, J. Fabito, N. Magnaye
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7083168771