The Agricultural IoT Based on Edge Computing project—under ASEAN-IVO in collaboration with NICT—has successfully concluded its final meeting in Da Lat City, Vietnam. Participating institutions from Vietnam, Malaysia, Thailand, and Japan convened to review outcomes, share experiences, and discuss the path forward.

The initiative aimed to tackle challenges in agriculture through an IoT framework that leverages edge computing. Key goals included developing real-time, low-latency data processing at farm sites; ensuring system security; and integrating advanced sensing, AI/ML innovations, and automation in farming systems. Among the work packages were smart fertigation & watering systems, plant disease predictors, pollinating systems, and drone-assisted monitoring.

Field demonstrations, such as visits to farms deploying sensor networks and automated systems, helped validate concepts in real settings. The project emphasized capacity building and cross-border cooperation, enabling the sharing of research methods, data, and lessons learnt among ASEAN countries.

Looking ahead, partners intend to build on the technical advances: improving disease-prediction models, strengthening privacy/security in IoT deployments, and expanding these solutions across diverse agricultural environments.

Further reading>> video compilation &  UTM newshub 

The IEEE 7th Symposium on Future Telecommunication Technologies (SOFTT 2025) became a significant platform for reinforcing the collaboration between the Faculty of Artificial Intelligence (FAI), Universiti Teknologi Malaysia (UTM), and Telkom University, Indonesia. Held at the Aruna Senggigi Hotel in Lombok, the symposium brought together academics and researchers from both institutions to share progress on joint projects under the Matching Grant Programme, an initiative anchored in the Memorandum of Understanding (MoU) signed between UTM and Telkom University.

During SOFTT 2025, UTM was represented by five delegates from the U-BAN Research Group. The progress update meeting and project symposium emphasized the ongoing Matching Grant collaboration between UTM and Telkom University, which is dedicated to advancing research in 6G technologies. The discussions highlighted significant progress in areas such as digital village connectivity, energy-efficient communication networks, and secure next-generation systems, reflecting the strong commitment of both institutions to jointly shape the future of telecommunication.

Further reading>> newshub UTM

In conjunction with the World Earth Day celebration, the Faculty of Artificial Intelligence (FAI), Universiti Teknologi Malaysia (UTM), in collaboration with Institut Sultan Iskandar (ISI), organised a community engagement programme at Kebun Bandar Sungai Bunus. The initiative involved staff, students, and the local community in a gotong-royong (communal work) activity aimed at promoting sustainability awareness and environmental responsibility.

The highlight of the event was the installation of a solar-powered automatic irrigation system developed using project funds, supporting UTM’s mission of promoting green innovation in urban agriculture. This system enhances water efficiency and reduces energy reliance, aligning with multiple Sustainable Development Goals (SDGs), including SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action).

This programme not only empowered the local community through environmental education and hands-on involvement but also served as a model for replicable green technology initiatives in other urban gardens. FAI UTM remains committed to integrating sustainability efforts with real-world community impact.

Further reading >> SG BUNUS

The Faculty of Artificial Intelligence (FAI), Universiti Teknologi Malaysia (UTM), conducted a site visit to the Smallholders Development Institute (IKPK) under the Rubber Industry Smallholders Development Authority (RISDA) in Pahang to explore potential collaboration in establishing an Artificial Intelligence (AI) Training Centre at RISDA Pahang. The visit aimed to discuss consultancy opportunities through which FAI UTM could support RISDA in upgrading its existing facilities into a fully equipped AI training centre that meets current technological needs and specifications.

During the discussion session, both parties examined the requirements for upgrading infrastructure and developing more modern and comprehensive facilities to support AI-driven training, research, and innovation in the agricultural sector in detail. FAI UTM also presented detailed proposals on the technical specifications and design of the training centre to ensure that it could serve as a national reference hub for the development and application of AI in agriculture.

Further reading >> RISDA

This project demonstrates how artificial intelligence (AI) and unmanned aerial vehicles (UAVs) can help advance sustainable agricultural practices. The team’s research utilizes UAV technology to monitor and detect plant diseases in rock melon greenhouses, significantly improving early intervention and reducing crop loss.

The award acknowledges the project’s significant contribution to the United Nations’ Sustainable Development Goals (SDGs), particularly in promoting sustainable agriculture, enhancing food security, and fostering innovation. The use of AI-driven UAVs in plant disease detection offers a scalable solution to a critical challenge farmers worldwide face, demonstrating the intersection of technology and sustainability.

The project was funded under the NICT ASEAN-IVO PJ2022 Project on Agricultural IoT based on Edge Computing. We aim to equip farmers with advanced tools for healthier crops and more sustainable farming practices. Winning this award reflects our team’s hard work and commitment. Using AI and UAV technology opens up new possibilities for precision agriculture. Early disease detection helps us minimize the impact on crop yield and quality, ultimately contributing to food security and sustainable farming.

FURTHER READING: https://news.utm.my/2024/08/uav-assisted-plant-disease-detection-wins-best-sdg-impact-award/