Smart Farming 2.0: IoT and Edge AI for Precision Crop Management and Sustainability

Dear Colleagues,

To design and implement an edge AI-enabled IoT framework that enables the precise, real-time monitoring and management of crop health, optimizing resource efficiency and advancing sustainable agricultural practices across diverse environments. 

This interdisciplinary research will accomplish the following objectives:

1. Technology Development

- Create low-cost IoT sensor nodes with edge computing capabilities to collect and process multispectral, soil, and environmental data locally, minimizing cloud dependency. 
- Develop lightweight machine learning models for decentralized anomaly detection, disease prediction, and nutrient deficiency identification.

2. Application and Scalability

- Test the system across varied crops (e.g., cereals, horticulture) and geographies (arid, tropical) to ensure adaptability.
- Integrate with existing farming practices, such as irrigation systems and drone-based monitoring, for seamless adoption.

3. Socioeconomic Impact

- Evaluate reductions in chemical/water usage, yield improvements, and cost savings for small-scale farmers.
- Partner with agricultural cooperatives to assess barriers to technology adoption and co-design user-friendly interfaces.

4. Sustainability Metrics

- Quantify environmental benefits (e.g., carbon footprint reduction, soil health preservation) and align outcomes with UN Sustainable Development Goals (SDGs 2, 12, 13).

Why It Is Novel and Relevant

- Multi-disciplinary fusion: combines agronomy, edge AI, IoT, and socioeconomics to address food security and climate resilience.
- Edge-first innovation: prioritizes decentralized, energy-efficient computing to empower rural and resource-limited settings.
- Real-world impact: bridges the gap between cutting-edge tech and practical farming needs, fostering equitable access to precision agriculture.

Dr. Chiang Liang Kok
Dr. Teck Kheng Lee
Dr. Howard Tang
Prof. Dr. Fanyi Meng
Topic Editors