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Innovative Approaches to Biodiversity and Ecology Monitoring: Artificial Intelligence (AI)...
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Innovative Approaches to Biodiversity and Ecology Monitoring: Artificial Intelligence (AI) and Drone Technology

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Ecology".

Deadline for manuscript submissions: 28 May 2026 | Viewed by 5807

Special Issue Editor

Dr. Eben N. Broadbent

E-Mail Website
Guest Editor
Spatial Ecology and Conservation Lab, School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32603, USA
Interests: remote sensing; forest ecology; conservation biology; airborne sensors; GatorEye; landscape simulation models; ecosystem and canopy ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of drone technology has revolutionized the field of ecological research, providing unprecedented opportunities for monitoring biodiversity and ecosystem health. Drones equipped with high-resolution cameras and sensors can capture detailed imagery of landscapes, enabling researchers to analyze and interpret ecological patterns at scales previously unattainable. The integration of Artificial Intelligence (AI) in processing drone imagery enhances the capability to extract meaningful information, facilitating the assessment of species distribution, habitat quality, and environmental changes. This research area is crucial as it supports conservation efforts, informs policy decisions, and contributes to our understanding of ecological dynamics in the face of climate change.

The goal of this Special Issue is to collect papers (original research articles and review papers) that provide insights into the application of AI techniques in analyzing drone imagery for biodiversity and ecological studies. By fostering interdisciplinary collaboration between ecologists, data scientists, and remote sensing experts, we aim to highlight innovative methodologies, case studies, and the implications of AI-driven analyses for ecological research and conservation strategies.

This Special Issue will welcome manuscripts that link the following themes:

  • AI Techniques in Image Processing: Studies focusing on the development and application of machine learning algorithms for analyzing drone imagery.
  • Biodiversity Monitoring: Research demonstrating the use of drones and AI to assess species richness, abundance, and distribution patterns.
  • Habitat Mapping and Assessment: Papers exploring how AI can enhance habitat classification and ecological modeling using drone data.
  • Ecological Applications: Case studies showcasing practical applications of drone imagery and AI in conservation, land management, and ecological restoration.
  • Data Integration and Analysis: Contributions discussing methods for integrating drone imagery with other data sources (e.g., satellite imagery, field surveys) to enhance ecological insights.

We look forward to receiving your original research articles and reviews.

Dr. Eben N. Broadbent
Guest Editor

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones 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 2600 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

  • drone imagery
  • artificial intelligence
  • biodiversity monitoring
  • ecological assessment
  • remote sensing
  • machine learning
  • habitat mapping
  • species distribution
  • conservation technology
  • environmental change

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

Published Papers (4 papers)

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24 pages, 3017 KB  
Article
Preliminary Findings of a Novel Thermal Drone-Based and AI Approach to Sampling Mesopredator Behaviour and Habitat Use
by Katrine Møller-Lassesen, Esther Magdalene Ellersgaard Enevoldsen, Cino Pertoldi and Sussie Pagh
Drones 2026, 10(6), 401; https://doi.org/10.3390/drones10060401 (registering DOI) - 22 May 2026
Abstract
Habitat selection is often activity-specific, as animals may use different environments depending on whether they are foraging, breeding, or moving through the habitat. Behavioural studies of nocturnal species are challenging, and conventional methods are limited in their applicability. In this study, we tested [...] Read more.
Habitat selection is often activity-specific, as animals may use different environments depending on whether they are foraging, breeding, or moving through the habitat. Behavioural studies of nocturnal species are challenging, and conventional methods are limited in their applicability. In this study, we tested a thermal drone in combination with Artificial Intelligence (AI) for focal animal sampling and habitat use of mesopredators. A drone mounted with a thermal video camera recorded the movements and behaviours of red foxes (Vulpes vulpes), European badgers (Meles meles), and Eurasian otters (Lutra lutra), while simultaneously geocoding their position. Additionally, we tested an AI-based analysis, LabGym for species and behaviour detection of video recordings. In Danish agricultural areas, both habitat separation and spatial overlap among the three mesopredators, were observed. Foxes showed a higher degree of versatility in both behaviour and habitat choice compared to badgers and otters. Otters were primarily found near water bodies, while badgers preferred foraging under tree cover and in meadows. The optimised LabGym achieved 80.4% mAP for species identification and successfully classified four behaviours with more than 80% accuracy. Using the thermal drone in combination with geolocation data and AI enables spatial mapping of mesopredator activities, adding valuable insights into predator ecology. Full article
(This article belongs to the Special Issue Innovative Approaches to Biodiversity and Ecology Monitoring: Artificial Intelligence (AI) and Drone Technology)
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25 pages, 2877 KB  
Article
Integration of Field Data and UAV Imagery for Coffee Yield Modeling Using Machine Learning
by Sthéfany Airane dos Santos Silva, Gabriel Araújo e Silva Ferraz, Vanessa Castro Figueiredo, Margarete Marin Lordelo Volpato, Danton Diego Ferreira, Marley Lamounier Machado, Fernando Elias de Melo Borges and Leonardo Conti
Drones 2025, 9(10), 717; https://doi.org/10.3390/drones9100717 - 16 Oct 2025
Cited by 2 | Viewed by 1612
Abstract
The integration of machine learning (ML) techniques with unmanned aerial vehicle (UAV) imagery holds strong potential for improving yield prediction in agriculture. However, few studies have combined biophysical field variables with UAV-derived spectral data, particularly under conditions of limited sample size. This study [...] Read more.
The integration of machine learning (ML) techniques with unmanned aerial vehicle (UAV) imagery holds strong potential for improving yield prediction in agriculture. However, few studies have combined biophysical field variables with UAV-derived spectral data, particularly under conditions of limited sample size. This study evaluated the performance of different ML algorithms in predicting Arabica coffee (Coffea arabica) yield using field-based biophysical measurements and spectral variables extracted from multispectral UAV imagery. The research was conducted over two crop seasons (2020/2021 and 2021/2022) in a 1.2-hectare experimental plot in southeastern Brazil. Three modeling scenarios were tested with Random Forest, Gradient Boosting, K-Nearest Neighbors, Multilayer Perceptron, and Decision Tree algorithms, using Leave-One-Out cross-validation. Results varied considerably across seasons and scenarios. KNN performed best with raw data, while Gradient Boosting was more stable after variable selection and synthetic data augmentation with SMOTE. Nevertheless, limitations such as small sample size, seasonal variability, and overfitting, particularly with synthetic data, affected overall performance. Despite these challenges, this study demonstrates that integrating UAV-derived spectral data with ML can support yield estimation, especially when variable selection and phenological context are carefully addressed. Full article
(This article belongs to the Special Issue Innovative Approaches to Biodiversity and Ecology Monitoring: Artificial Intelligence (AI) and Drone Technology)
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24 pages, 2714 KB  
Article
Drone Monitoring and Behavioral Analysis of White-Beaked Dolphins (Lagenorhynchus albirostris)
by Ditte Grønnegaard Lauridsen, Niels Madsen, Sussie Pagh, Maria Glarou, Cino Pertoldi and Marianne Helene Rasmussen
Drones 2025, 9(9), 651; https://doi.org/10.3390/drones9090651 - 16 Sep 2025
Cited by 2 | Viewed by 1871
Abstract
Marine mammals serve as indicator species for environmental and human health. However, they are increasingly exposed to pressure from human activities and climate change. The white-beaked dolphin (Lagenorhynchus albirostris) (WBD) is among the species negatively affected by these conditions. To support [...] Read more.
Marine mammals serve as indicator species for environmental and human health. However, they are increasingly exposed to pressure from human activities and climate change. The white-beaked dolphin (Lagenorhynchus albirostris) (WBD) is among the species negatively affected by these conditions. To support conservation and management efforts, a deeper understanding of their behavior and movement patterns is essential. One approach is drone-based monitoring combined with artificial intelligence (AI), allowing efficient data collection and large-scale analysis. This study aims to: (1) investigate the use of drone imagery and AI to monitor and analyze marine mammal behavior, and (2) test the application of machine learning (ML) to identify behavioral patterns. Data were collected in Skjálfandi Bay, Iceland, between 2021 and 2023. Three behavioral types were identified: Traveling, Milling, and Respiration. The AI_RGB model showed high performance on Traveling behavior (precision 92.3%, recall 96.9%), while the AI_gray model achieved higher precision (97.3%) but much lower recall (9.5%). The model struggled to classify Respiration accurately (recall 1%, F1-score 2%). A key challenge was misidentification of WBDs due to visual overlap with birds, waves, and reflections, resulting in high false positive rates. Multimodal AI systems may help reduce such errors in future research. Full article
(This article belongs to the Special Issue Innovative Approaches to Biodiversity and Ecology Monitoring: Artificial Intelligence (AI) and Drone Technology)
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30 pages, 2666 KB  
Systematic Review
Harnessing Regenerative Agriculture, Unmanned Aerial Systems, and AI for Sustainable Cocoa Farming in West Africa
by Andrew Manu, Jeff Dacosta Osei, Vincent Kodjo Avornyo, Thomas Lawler and Kwame Agyei Frimpong
Drones 2026, 10(1), 75; https://doi.org/10.3390/drones10010075 - 22 Jan 2026
Viewed by 1286
Abstract
Cocoa production in West Africa supplies most of the global demand but is increasingly constrained by yield stagnation, soil degradation, disease pressure, and climate variability. This review examines how integrating regenerative agriculture (RA) with unmanned aerial systems (UAS) and artificial intelligence (AI) can [...] Read more.
Cocoa production in West Africa supplies most of the global demand but is increasingly constrained by yield stagnation, soil degradation, disease pressure, and climate variability. This review examines how integrating regenerative agriculture (RA) with unmanned aerial systems (UAS) and artificial intelligence (AI) can support more precise and resilient cocoa management across heterogeneous smallholder landscapes. A PRISMA-guided systematic review of peer-reviewed literature published between 2000 and 2024 was conducted, yielding 49 core studies analyzed alongside supporting evidence. The synthesis evaluates regenerative agronomic outcomes, UAV-derived multispectral, thermal, and structural diagnostics, and AI-based analytical approaches for stress detection, yield estimation, and management zoning. Results indicate that regenerative practices consistently improve soil health and yield stability, while UAS data enhance spatial targeting of rehabilitation, shade management, and stress interventions. AI models further improve predictive capacity and decision relevance when aligned with data availability and institutional context, although performance varies across systems. Reported yield stabilization or improvement typically ranges from 12–30% under integrated approaches, with concurrent reductions in fertilizer and water inputs where spatial targeting is applied. The review concludes that effective scaling of RA–UAS–AI systems depends less on technical sophistication than on governance arrangements, extension integration, and cooperative service models, positioning these tools as enabling components rather than standalone solutions for sustainable cocoa intensification. Full article
(This article belongs to the Special Issue Innovative Approaches to Biodiversity and Ecology Monitoring: Artificial Intelligence (AI) and Drone Technology)
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