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Informatics
Special Issues
Revolutionizing Agriculture and Natural Resource Management with Artificial Intelligence Approaches
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Revolutionizing Agriculture and Natural Resource Management with Artificial Intelligence Approaches

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

Deadline for manuscript submissions: 28 February 2027 | Viewed by 4677

Editors

Dr. Sarawut Ninsawat

E-Mail Website
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
Dr. Jaturong Som-ard

E-Mail Website
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

24 pages, 5403 KB  
Article
Reliability Reserve: A Markov Chain-Based Metric for Real-Time Operator Decision Support in Ayran Fermentation
by Zhanagul Doumchariyeva, Jamalbek Tussupov, Madina Sambetbayeva, Tamara Zhukabayeva, Madina Yessenaliyeva, Begzhan Kalemshariv, Sagi Issayev and Munaram Khassanova
Informatics 2026, 13(7), 105; https://doi.org/10.3390/informatics13070105 - 3 Jul 2026
Viewed by 513
Abstract
This study presents a Markov chain-based metric called the Reliability Reserve (τ), designed to estimate the time available for operator intervention during the ayran fermentation process. This indicator can be integrated into a digital twin forecast management system. The fermentation process was obtained [...] Read more.
This study presents a Markov chain-based metric called the Reliability Reserve (τ), designed to estimate the time available for operator intervention during the ayran fermentation process. This indicator can be integrated into a digital twin forecast management system. The fermentation process was obtained using a DTMC (discrete-time Markov chain) and divided into five states according to pH (S1–S5). Laboratory samples were prepared from premium-grade cow’s milk sourced from the Zher-Ana farm and divided into three experimental groups: Control (without additives), Opt1 (3% additive), and Opt2 (4% additive). A sequence of states was created for the three studied groups (Control, Opt1, and Opt2), and the transition states of the matrix were calculated. The Reliability Reserve quantifies how much time is left before the system transitions from the target state to the acidification state. For the first group, P45 was 0.200, corresponding to τ = 26.9 min. The incorporation of functional additives increased P45 to 0.250, reducing τ to 20.9 min and shortening the operator intervention window by approximately 6 min. Markov chains were constructed using 101 interpolated time points obtained from five experimental pH measurements for each group. The original pH values were recorded at 2, 4, 6, 8, and 10 h of fermentation and linearly interpolated with a step size of 0.1 h to improve temporal resolution. Model robustness was evaluated using sensitivity analysis (±0.05 pH boundary shifts) and bootstrap resampling (n = 1000, 95% confidence intervals). The concept of Reliability Reserve is a practical decision-making tool in real time. It offers an alternative to traditional reliability indicators such as MTTF and allows integration into digital twin-based control systems. Full article
(This article belongs to the Special Issue Revolutionizing Agriculture and Natural Resource Management with Artificial Intelligence Approaches)
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26 pages, 5700 KB  
Article
Ensuring High-Quality Rainfall Datasets in Thailand: A Multi-Step Quality Control Approach and Satellite-Based Evaluation
by Dusadee Pinasu and Apichon Witayangkurn
Informatics 2026, 13(6), 96; https://doi.org/10.3390/informatics13060096 - 18 Jun 2026
Viewed by 702
Abstract
Reliable, high-quality rainfall data are vital for soil and water management, crop forecasting, and risk assessment. These applications are essential for food security, climate resilience, biodiversity monitoring, and rural livelihoods. Rainfall monitoring in Thailand is challenging due to the limited density of official [...] Read more.
Reliable, high-quality rainfall data are vital for soil and water management, crop forecasting, and risk assessment. These applications are essential for food security, climate resilience, biodiversity monitoring, and rural livelihoods. Rainfall monitoring in Thailand is challenging due to the limited density of official stations and the inconsistent quality of data from multiple sources, compounded by calibration issues. This study introduces a comprehensive quality control (QC) approach tailored for the Thai context, presenting a systematic pipeline that clarifies the hierarchy and sequence of operations. The method uses rainfall data from 3075 stations of the Thai Meteorological Department (TMD) and the Thaiwater network. It includes basic QC for data completeness and advanced QC using a quality (Q) index to assess station reliability, diving the stations into five groups: poor (<50), moderate (50–80), acceptable (80–85), good (85–90), and excellent (>90). The results indicate that Thaiwater consistently achieved moderate to excellent Q index values, exceeding 70% annually, with values surpassing 90% in 2023. In contrast, the TMD maintained excellent quality, with values above 90% for all years. Out of over one million daily entries, 87% were verified as correct, though the Thaiwater data for 2024 showed only 70% accuracy. The QC procedures significantly improved data reliability, reducing the root mean square error for GSMaP and IMERG by 1.7% and 1.5%, respectively, and lowering the false alarm rate by approximately 0.001–0.002 without compromising heavy rainfall detection. A systematic QC framework is essential for ensuring high-quality datasets in rainfall applications. Full article
(This article belongs to the Special Issue Revolutionizing Agriculture and Natural Resource Management with Artificial Intelligence Approaches)
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16 pages, 11584 KB  
Article
Mapping Sub-Field Crop Water Use Dynamics Using OpenET Data and Zero-Shot Time-Series Foundation Model
by Chinmay Deval and Siddharth Chaudhary
Informatics 2026, 13(6), 95; https://doi.org/10.3390/informatics13060095 - 18 Jun 2026
Viewed by 577
Abstract
Precision agriculture increasingly relies on high-resolution, long-term remote sensing to delineate sub-field management zones. However, traditional spatial zonation assumes temporal stationarity, utilizing seasonal aggregates that obscure transient, intra-annual stress signals. This study develops a data-driven framework to characterize both persistent and non-stationary crop [...] Read more.
Precision agriculture increasingly relies on high-resolution, long-term remote sensing to delineate sub-field management zones. However, traditional spatial zonation assumes temporal stationarity, utilizing seasonal aggregates that obscure transient, intra-annual stress signals. This study develops a data-driven framework to characterize both persistent and non-stationary crop water use dynamics by integrating monthly, 30-m evapotranspiration (ET) data from OpenET (2000–2025) with zero-shot temporal anomaly detection. A pre-trained time-series foundation model (Chronos-T5-Small) generated counterfactual expectations for sub-field ET, quantifying deviations using a mean absolute error-based anomaly score. Unsupervised clustering of these anomaly scores with longitudinal ET metrics partitioned the landscape into dynamic biophysical regimes. Cross-registered against legacy persistence mapping based on seasonal totals, the foundation model showed strong directional agreement (86.1%, Cohen’s Kappa = 0.716) in identifying chronically constrained zones across 869 shared active pixels. Crucially, the framework identified 966 historically persistent pixels undergoing stability decay, of which 95.3% were statistically verified via paired t-tests to have collapsed into the field’s baseline variance pool. Furthermore, counterfactual anomaly detection isolated zones of recent acute divergence, differentiating enduring edaphic constraints from sudden system disruptions. This approach demonstrates how foundation models can transition from purely predictive engines to diagnostic instruments, advancing operational precision agriculture. Full article
(This article belongs to the Special Issue Revolutionizing Agriculture and Natural Resource Management with Artificial Intelligence Approaches)
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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
Viewed by 1999
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
(This article belongs to the Special Issue Revolutionizing Agriculture and Natural Resource Management with Artificial Intelligence Approaches)
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