Environmental & Agroecological Informatics Research Group

EAIR is an emerging academic research group exploring the intersection of Artificial Intelligence, Environmental Informatics, Biodiversity Informatics, and Agroecology**.

The group brings together research and student projects that apply computational methods to real-world environmental and agroecological challenges, particularly those relevant to Kalimantan (Borneo) and tropical ecosystems.

Research Focus

EAIR focuses on the development and application of intelligent computational methods for understanding, monitoring, and managing environmental and agroecological systems.

Our current research interests include:

• Biodiversity Informatics — computational approaches for biodiversity data, species recognition, knowledge discovery, and biodiversity information systems.
• Environmental Intelligence — AI, data analytics, and remote sensing for understanding environmental conditions and changes.
• Agroecological Informatics — intelligent sensing, monitoring, and data-driven approaches for sustainable agroecosystems.
• Open-World Artificial Intelligence — intelligent systems capable of recognizing unknown information and adapting to changing environments.
• Edge AI & Environmental IoT — lightweight AI and sensing systems for field and environmental applications.

Research Projects

KalimantanBio (https://kalimantanbio.com)

KalimantanBio is a biodiversity information platform designed to organize and provide access to information about species found in Kalimantan.

Current development explores intelligent biodiversity knowledge systems, including species search, species relationships, taxonomy exploration, comparative species analysis, and scientific literature connections.

Open-World Lifelong Plant Recognition ([https://kalimantanbio.com/identifier/])(https://kalimantanbio.com/identifier/)

Open-World Lifelong Plant Recognition explores AI systems capable of recognizing known plant species, detecting previously unknown species, and progressively learning new species over time.

The project investigates open-set recognition, novelty detection, and continual learning for plant species recognition, with a particular focus on the biodiversity of Borneo.

The system is integrated with KalimantanBio to explore a longer-term vision of biodiversity AI that can continuously expand its knowledge as new species data become available.

Smart Soil IoT

Smart Soil IoT explores low-cost sensing technologies for monitoring soil and environmental conditions.

The project investigates the integration of soil sensors, environmental sensors, edge computing, and data analytics for applications in tropical and wetland agroecosystems.

Aerial Analytics

Aerial Analytics explores computational methods for extracting environmental and agroecological information from drone and aerial imagery.

Research topics include vegetation monitoring, land-cover analysis, image segmentation, environmental change detection, and AI-assisted aerial image analysis.

Student Research

Student participation is an important part of EAIR.

Undergraduate students contribute through research projects, course projects, software development, data collection, experimentation, and exploratory studies.

EAIR aims to provide an environment where students can experience the complete research process:

Promising student projects may develop further into datasets, open-source software, research prototypes, publications, or undergraduate research.

Research Outputs

EAIR aims to produce research outputs that are useful both academically and practically, including:

• Scientific publications
• Biodiversity and environmental datasets
• Open-source software
• Research prototypes
• Environmental monitoring systems
• Student research
• Technical and scientific communication

Collaboration

EAIR welcomes collaboration with researchers, students, institutions, and organizations interested in Artificial Intelligence, biodiversity, environmental monitoring, agroecology, remote sensing, and related interdisciplinary areas.

Potential collaborations may include joint research, dataset development, student research, field studies, research software, and interdisciplinary projects.

For collaboration inquiries, please contact gusti.alfarisy@gmail.com or connect with me through the Profile menu in the navigation bar.