Revolutionizing Agriculture and Natural Resource Management with Artificial Intelligence Approaches

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: 31 July 2026 | Viewed by 449

Special Issue Editors


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Guest Editor
Department of Information and Communications Technologies (ICT), Asian Institute of Technology, Bangkok 12120, Thailand
Interests: machine learning; deep learning; big earth data; crop classification; mobility analysis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Geography, Faculty of Humanities and Social Sciences, Mahasarakham University, Kantharawichai, Maha Sarakham 44150, Thailand
Interests: remote sensing; earth observation; UAV photogrammetry; machine learning; crop type/LC classification; digital agriculture

Special Issue Information

Dear Colleagues,

Agriculture and natural resource systems lie at the nexus of food security, climate resilience, biodiversity, and rural livelihoods. Rapid advances in artificial intelligence (AI)—with a particular emphasis on machine learning (ML) and deep learning (DL)—from GeoAI and foundation models to AI-enabled sensing at the edge are transforming how we observe, understand, and steward working landscapes.

This Special Issue of Informatics examines how AI is reshaping agriculture and natural resource management through GeoAI, AIoT, computer vision, UAVs, and field robotics. We invite original research and comprehensive review articles that advance theoretical understanding, propose novel methodologies, or present innovative practical AI approaches that turn Big Earth Data from Earth observation (EO)-based satellites, UAVs, and in situ/AIoT sensors into decision-ready insights for understanding crop planning, crop health, yield forecasting, soil and water management, biodiversity monitoring, and risk assessment. By uniting theoretical advances in AI with field-ready systems, this Special Issue aims to chart new directions across GeoAI, AIoT, computer vision, UAVs, and field robotics for agriculture and natural resource management.

We welcome submissions on, but not limited to, the following topics:

  • Big Earth Data and GeoAI for crop health, crop yield, soil health, evapotranspiration, and drought/stress monitoring;
  • AI approaches of spatiotemporal forecasting for agro-ecological systems;
  • Edge and AIoT systems for scalable field analytics;
  • Computer vision for disease/weed detection, phenotyping, and quality grading;
  • Autonomous, multi-modal UAV systems from sensing to actuation;
  • Field robotics: autonomous scouting and precision spraying/harvesting;
  • AI applications for water allocation/quality monitoring; watershed-scale modeling and decision support;
  • The role of AI in biodiversity assessment, restoration monitoring, and anti-deforestation analytics;
  • Integrating AI approaches together with remote sensing data such as satellite, weather stations and in situ data in monitoring health management and sustainable agricultural practices.

Dr. Sarawut Ninsawat
Dr. Jaturong Som-ard
Guest Editors

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Keywords

  • agriculture
  • natural resource management
  • big earth data
  • artificial intelligence
  • AIoT
  • computer vision

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Published Papers (1 paper)

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Research

20 pages, 4455 KB  
Article
The Relevance of Compound Events in Bee Traffic Monitoring
by Andrea Nieves-Rivera, Marie Lluberes-Contreras and Rémi Mégret
Informatics 2026, 13(5), 65; https://doi.org/10.3390/informatics13050065 - 23 Apr 2026
Abstract
Bees are essential pollinators for agricultural systems, making accurate, automated monitoring of their behavior critical for assessing colony health and ecosystem stability. Recent advances in computer vision and artificial intelligence have enabled large-scale bee traffic monitoring at hive entrances; however, most existing event [...] Read more.
Bees are essential pollinators for agricultural systems, making accurate, automated monitoring of their behavior critical for assessing colony health and ecosystem stability. Recent advances in computer vision and artificial intelligence have enabled large-scale bee traffic monitoring at hive entrances; however, most existing event classification methods focus exclusively on simple entrance and exit events. This simplification overlooks compound movements—such as U-turns and guarding behaviors—that represent a substantial portion of bee activity and can lead to inaccurate trajectory reconstruction and misleading behavioral interpretations. In this work, we systematically analyze existing event classification strategies used in automatic bee traffic monitoring, evaluating their performance on both simple and compound movements. We then propose extended classification methods that explicitly model compound events by incorporating bidirectional movement patterns derived from positional and angular cues. Using a manually annotated dataset of computer-vision-based hive entrance recordings, we compare threshold-based, displacement-based, and angle-based approaches under simple and mixed-event conditions. Our results demonstrate that compound events account for over one-third of all detected movements and that classification methods explicitly designed to handle bidirectional behavior substantially outperform traditional approaches in both accuracy and robustness. In particular, threshold-based bidirectional classification achieves near-perfect performance when full trajectories are available, while displacement-based methods provide a reliable alternative under partial observations. These findings highlight the importance of modeling compound behaviors in automated bee monitoring systems and contribute to more accurate flight reconstruction, behavioral analysis, and AI-driven decision support for precision agriculture and pollinator management. Full article
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