Alternative monitoring system for wolffia globosa cultivation using iot and image-based diagnostics

Name: Pavadol Dhechasidphaisan

Organization : King Mongkut’s University of Technology Thonburi. School of Information Technology

Name: Phae, Kyar

Organization : King Mongkut’s University of Technology Thonburi. School of Information Technology

Name: Pongkhun Poonsupsopon

Organization : King Mongkut’s University of Technology Thonburi. School of Information Technology

Name: Pornchai Mongkolnam

Organization : King Mongkut’s University of Technology Thonburi. School of Information Technology

Name: Watanyoo Suksa-ngiam

Organization : King Mongkut’s University of Technology Thonburi. School of Information Technology

Email : watanyoo.suk@kmutt.ac.th

keyword: Smart farming

LCSH: Duckweeds -- Thailand

LCSH: Internet of things -- Agricultural applications

LCSH: Water quality -- Monitoring -- Data processing

LCSH: Image processing -- Digital techniques

Abstract: Wolffia globosa, a fast-growing duckweed species, is emerging as a high-value alternative protein source in Thailand; however, small and medium scale farmers continue to rely on manual observation to assess water quality, contamination risks, and pond health. Such practices are time consuming, prone to error, and often lack practical guidance, causing unreliable yields and increased food safety risks. DuWeeNext is a low-cost, mobile-first monitoring system designed as a smart farming companion for Wolffia globosa cultivation. The system aims to facilitate accurate measurement of key pond environmental parameters, including pH, temperature, and total dissolved solids (TDS), while reducing farmers’ reliance on manual and subjective observation. DuWeeNext integrates three core modules: (1) an IoT-based water quality monitoring module that collects real-time sensor data; (2) an image-based pond diagnosis module that classifies smartphone images into health-related categories and provides rule-based recommendations; and (3) an educational module that supports contamination risk reduction by guiding farmers in recognizing and circumventing potential sources such as pesticide runoff, heavy metals, fertilizer overuse, and unsafe organic inputs. System architecture and implementation are described, and performance is evaluated using long-term sensor data logs, image classification metrics, and farmer usability feedback. Results from the prototype deployment indicate that DuWeeNext enhances water quality awareness, supports earlier detection of pond abnormalities, and improves farmers’ understanding of contamination risks, offering a practical, scalable monitoring alternative for Wolffia globosa cultivation.

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2026

Modified: 2026-08-05

Issued: 2026-08-05

บทความ/Article

application/pdf

BibliograpyCitation : In ECTI Association and Burapha University. Faculty of Engineering. 2026 23rd International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2026) (pp.151-156). Piscataway, NJ : IEEE

eng

©copyrights King Mongkut's University of Technology North Bangkok

ลำดับที่.	ชื่อแฟ้มข้อมูล	ขนาดแฟ้มข้อมูล	จำนวนเข้าถึง	วัน-เวลาเข้าถึงล่าสุด
1	ECTI-CON 2026pp.151-156.pdf	1.43 MB

ใช้เวลา

0.057808 วินาที