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Forests
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Application and Development of Artificial Intelligence Technology in Forestry Management
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Application and Development of Artificial Intelligence Technology in Forestry Management

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (16 June 2026) | Viewed by 1218

Editors

Dr. Weijun Xie

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
Interests: intelligent processing of agricultural and forestry products
Dr. Ying Liu

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
Interests: forestry engineering
Dr. Deyong Yang

E-Mail Website
Guest Editor
College of Engineering, National R & D Center for Agro-Processing Equipment, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, China
Interests: intelligent processing of agricultural and forestry products

Special Issue Information

Dear Colleagues,

Research into forestry operations is pivotal to global ecological security and sustainable development. Forests, covering 31% of Earth's land area, sustain 80% of terrestrial biodiversity and support the livelihoods of over 1.6 billion people. However, climate change, wildfires, and pests contribute to an annual loss of 10 million hectares of forests, exacerbating biodiversity collapse and carbon sink degradation. Optimizing forest operations through technology-driven strategies has therefore become critical. Developing scalable solutions that balance economic viability with ecological preservation requires urgent interdisciplinary research into AI-enhanced monitoring, precision harvesting, and adaptive management frameworks.

This Special Issue, “Application and Development of Artificial Intelligence Technology in Forestry Operations”, aims to explore cutting-edge advancements in AI-driven solutions for sustainable forest management. By integrating technologies such as machine learning, the IoT, and remote sensing, AI is revolutionizing forest management. This Special Issue seeks to compile innovative research on AI applications in forestry, addressing challenges such as forest resource monitoring and management, forest cultivation and management, forest protection, disaster prevention, and wildlife conservation.

This Special Issue prioritizes research studies addressing the AI–forestry nexus, particularly those that bridge the gap between algorithmic innovation and field implementation. By fostering convergence across ecological science, data engineering, and policy design, we aim to establish AI as indispensable to next-generation sustainable forest management.

Dr. Weijun Xie
Dr. Ying Liu
Dr. Deyong Yang
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. Forests 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

  • precision breeding
  • phenotypic analysis
  • forest mapping
  • wildfire monitoring
  • pest management
  • autonomous harvesting systems
  • defect detection
  • wildlife conservation
  • forest food resource management

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

Published Papers (2 papers)

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Research

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18 pages, 2201 KB  
Article
Automated Classification and Explainability of Cedar (Cedrela montana) and Cinchona (Cinchona pubescens) Using Deep Learning and Grad-CAM: A Case Study in the Amazon Region of Northern Peru
by Heling Kristtel Masgo Ventura, Jhosymar Bacalla Tenorio, Roberto Carlos Santa Cruz Acosta, Victor Gerardo Inga Merino, Carlos Luis Lobatón Arenas, Pompeyo Ferro, Euclides Ticona Chayña, José Marchena-Dioses, Tito Sanchez-Santillan and Eli Morales-Rojas
Forests 2026, 17(7), 779; https://doi.org/10.3390/f17070779 - 30 Jun 2026
Viewed by 161
Abstract
Accurate identification of timber species is essential for sustainable forest management and the prevention of illegal logging, particularly in regions such as the Peruvian Amazon. This study evaluated explainable deep learning models for the classification of cedar (Cedrela montana) and cinchona [...] Read more.
Accurate identification of timber species is essential for sustainable forest management and the prevention of illegal logging, particularly in regions such as the Peruvian Amazon. This study evaluated explainable deep learning models for the classification of cedar (Cedrela montana) and cinchona (Cinchona pubescens) using macro-images of transverse wood sections collected in the Amazonas region of northern Peru. A dataset of wood images was expanded through a sliding-window patch extraction strategy and used to train and compare three architectures: a custom Convolutional Neural Network (CNN), MobileNetV2, and EfficientNetB0. Model performance was assessed using accuracy, precision, recall, and F1-score, while Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to interpret the visual regions influencing predictions. All models achieved high classification performance. EfficientNetB0 obtained the best results, reaching 100% accuracy, precision, recall, and F1-score, while MobileNetV2 and the custom CNN achieved near-perfect performance. Training and validation curves indicated stable convergence without significant overfitting. Grad-CAM analysis showed that the custom CNN generated more localized and interpretable activation regions, whereas MobileNetV2 and EfficientNetB0 focused on broader textural patterns, revealing different feature-learning strategies. These findings demonstrate the potential of explainable deep learning for automated wood identification and support applications in forest monitoring, biodiversity conservation, and illegal logging control. Full article
(This article belongs to the Special Issue Application and Development of Artificial Intelligence Technology in Forestry Management)

Review

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36 pages, 2407 KB  
Review
Monitoring Carbon Stock Change at the Individual-Plant Scale: A Methodological Review and Integrative Framework
by Ruiying Ren, Kai Zhang, Liang Qi, Maocheng Zhao, Weijun Xie, Chi Zhou and Mingguang Li
Forests 2026, 17(5), 563; https://doi.org/10.3390/f17050563 - 4 May 2026
Viewed by 275
Abstract
With increasing demand for fine-scale ecological management under carbon neutrality frameworks, multi-temporal assessment of carbon stock change (ΔC) at the individual-plant scale has become essential for understanding plant-level carbon dynamics and supporting management decisions. However, methodologies for repeated monitoring at this scale remain [...] Read more.
With increasing demand for fine-scale ecological management under carbon neutrality frameworks, multi-temporal assessment of carbon stock change (ΔC) at the individual-plant scale has become essential for understanding plant-level carbon dynamics and supporting management decisions. However, methodologies for repeated monitoring at this scale remain fragmented, showing limited cross-temporal comparability, weak cross-scale consistency, and insufficient integration across methods. Existing approaches can be grouped into three pathways: (i) process-based methods derived from CO2 exchange measurements, (ii) state-based approaches estimating biomass and ΔC, and (iii) sensing-based approaches using structural, spectral, thermal, and fluorescence signals. These approaches offer complementary strengths, yet none simultaneously achieve high accuracy, temporal continuity, and operational scalability for multi-temporal ΔC estimation. Among these, stock-based and structural approaches form the primary estimation pathways, while flux-based and functional sensing methods provide complementary constraints. This review synthesizes and compares these approaches in terms of their theoretical basis, spatial support, temporal characteristics, and uncertainty structures. To address the lack of methodological integration, we propose a structure–function–scale framework that links heterogeneous observations across spatial and temporal domains and emphasizes cross-scale consistency as a prerequisite for reliable ΔC estimation. Within this framework, we further examine how multi-source integration can connect structural and functional observations through segmentation, co-registration, scaling, temporal alignment, and uncertainty propagation. By integrating traditional measurement logic with emerging remote sensing technologies, this review provides a unified methodological framework for ΔC estimation and identifies key directions for advancing fine-scale carbon monitoring, spatiotemporally consistent data fusion, uncertainty-aware inference, and MRV-oriented verification systems. Full article
(This article belongs to the Special Issue Application and Development of Artificial Intelligence Technology in Forestry Management)
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