Special Issue "Electronics, Close-Range Sensors and Artificial Intelligence in Forestry"

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: 1 December 2021.

Special Issue Editors

Prof. Dr. Stelian Alexandru Borz
E-Mail Website
Guest Editor
Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, Şirul Beethoven 1, Brasov 500123, Romania
Interests: wood and biomass supply chain optimization; sensor technology; transport optimization; forest planning
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Dr. Andrea R. Proto
E-Mail Website
Guest Editor
Department of Agraria, Mediterranean University of Reggio Calabria, 89122 Reggio Calabria, Italy
Interests: forest mechanization; productivity; NDT evaluation and wood quality; measuring wood properties; wood technology; wood engineering; urban forestry; agro-forestry biomass; sustainable agro-forestry management
Special Issues and Collections in MDPI journals
Dr. Robert Keefe
E-Mail Website
Guest Editor
College of Natural Resources, 875 Perimeter Drive MS 1133, University of Idaho Experimental Forest, Moscow, ID 83844-1133, USA
Dr. Mihai Nita
E-Mail Website
Guest Editor
Department of Forest Engineering, Universitatea Transilvania Brasov, 500036 Braşov, Romania
Interests: remote sensing; GIS; forest and water; forest management; machine learning
Special Issues and Collections in MDPI journals

Special Issue Information

Dear colleagues, 

The use of electronics, close-range sensing and artificial intelligence has changed the management paradigm in many of the current industries, in which big-data analytics by automated processes has become the backbone of decision making and improvement. Acknowledging the integration of electronics, devices, sensors and intelligent algorithms in many of the equipment used in forest operations, as well as their use in various forestry-related applications, reality is showing us that many of the applications of forest engineering still rely on data collected traditionally, which is resource-intensive, demanding analysis and, in many cases, being limited to establishing the specific behaviors of forest product systems and wood supply chain. This situation is often preventing from developing solutions for improvement or to accurately infer the laws behind the operation and management of such systems. In particular, partly mechanized systems, environmental impact assessment and ergonomics of operation are components or disciplines in which improvement is crucial and which could rely on new advancements in algorithms, computers, electronics and sensor science to find solutions, by robust solutions, to these pressing needs. This Special Issue is intended to cover the whole range of applications, as is typical to forestry and forest engineering in which one or more of the following could be addressed by high-quality research or review papers:

  • Moving from traditional to computer-aided time consumption and productivity studies and models, and from traditional to advanced methods to estimate forest growth, production, dynamics and disturbance;
  • Enhancing big-data collection, analysis and augmentation using integrated or external electronics, computers and sensor systems;
  • Development, validation, and use of human and forestry equipment activity recognition models developed using smart watches, smart phones, micro-electric sensors and Internet-of-Things devices;
  • Applications of virtual reality, teleoperation, telematics, and robotics in forestry;
  • Application of close-range sensing to solve important problems in forestry and forest operations, such as those related to productivity, ergonomics and environmental impact assessment;
  • Development and/or implementation of intelligent algorithms and artificial intelligence (AI) for multivariate analyses, classification and event-based management in forestry and forest engineering;
  • Adapting and implementing low-cost solutions to answer the pressing problems in the wood supply chain and to overcome bottlenecks;
  • Intelligent methods, devices, equipment, machines, protocols, processes and robots that have a direct application or use in forestry;
  • Effects of automation on the environment and human welfare.
Prof. Dr. Stelian Alexandru Borz
Dr. Andrea Rosario Proto
Dr. Robert Keefe
Dr. Mihai Nita
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 papers will be 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 100 words) can be sent to the Editorial Office for announcement on this website.

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. 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 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

  • forest operations
  • productivity
  • ergonomics
  • environmental impact assessment
  • close-range
  • sensors
  • big data
  • virtual reality
  • teleoperation
  • automation
  • artificial intelligence
  • forestry 4.0

Published Papers (3 papers)

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Research

Article
A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN
Forests 2021, 12(6), 768; https://doi.org/10.3390/f12060768 - 10 Jun 2021
Viewed by 687
Abstract
Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, [...] Read more.
Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods. Full article
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Article
Development of a Modality-Invariant Multi-Layer Perceptron to Predict Operational Events in Motor-Manual Willow Felling Operations
Forests 2021, 12(4), 406; https://doi.org/10.3390/f12040406 - 29 Mar 2021
Viewed by 455
Abstract
Motor-manual operations are commonly implemented in the traditional and short rotation forestry. Deep knowledge of their performance is needed for various strategic, tactical and operational decisions that rely on large amounts of data. To overcome the limitations of traditional analytical methods, Artificial Intelligence [...] Read more.
Motor-manual operations are commonly implemented in the traditional and short rotation forestry. Deep knowledge of their performance is needed for various strategic, tactical and operational decisions that rely on large amounts of data. To overcome the limitations of traditional analytical methods, Artificial Intelligence (AI) has been lately used to deal with various types of signals and problems to be solved. However, the reliability of AI models depends largely on the quality of the signals and on the sensing modalities used. Multimodal sensing was found to be suitable in developing AI models able to learn time and location-related data dependencies. For many reasons, such as the uncertainty of preserving the sensing location and the inter- and intra-variability of operational conditions and work behavior, the approach is particularly useful for monitoring motor-manual operations. The main aim of this study was to check if the use of acceleration data sensed at two locations on a brush cutter could provide a robust AI model characterized by invariance to data sensing location. As such, a Multi-Layer Perceptron (MLP) with backpropagation was developed and used to learn and classify operational events from bimodally-collected acceleration data. The data needed for training and testing was collected in the central part of Romania. Data collection modalities were treated by fusion in the training dataset, then four single-modality testing datasets were used to check the performance of the model on a binary classification problem. Fine tuning of the regularization parameters (α term) has led to acceptable testing and generalization errors of the model measured as the binary cross-entropy (log loss). Irrespective of the hyperparameters’ tunning strategy, the classification accuracy (CA) was found to be very high, in many cases approaching 100%. However, the best models were those characterized by α set at 0.0001 and 0.1, for which the CA in the test datasets ranged from 99.1% to 99.9% and from 99.5% to 99.9%, respectively. Hence, data fusion in the training set was found to be a good strategy to build a robust model, able to deal with data collected by single modalities. As such, the developed MLP model not only removes the problem of sensor placement in such applications, but also automatically classifies the events in the time domain, enabling the integration of data collection, handling and analysis in a simple less resource-demanding workflow, and making it a feasible alternative to the traditional approach to the problem. Full article
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Article
A Forest Fire Detection System Based on Ensemble Learning
Forests 2021, 12(2), 217; https://doi.org/10.3390/f12020217 - 13 Feb 2021
Cited by 7 | Viewed by 1284
Abstract
Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging task. The traditional image processing method relies heavily on manmade features, which is not universally applicable to all forest scenarios. In order to solve this problem, the [...] Read more.
Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging task. The traditional image processing method relies heavily on manmade features, which is not universally applicable to all forest scenarios. In order to solve this problem, the deep learning technology is applied to learn and extract features of forest fires adaptively. However, the limited learning and perception ability of individual learners is not sufficient to make them perform well in complex tasks. Furthermore, learners tend to focus too much on local information, namely ground truth, but ignore global information, which may lead to false positives. In this paper, a novel ensemble learning method is proposed to detect forest fires in different scenarios. Firstly, two individual learners Yolov5 and EfficientDet are integrated to accomplish fire detection process. Secondly, another individual learner EfficientNet is responsible for learning global information to avoid false positives. Finally, detection results are made based on the decisions of three learners. Experiments on our dataset show that the proposed method improves detection performance by 2.5% to 10.9%, and decreases false positives by 51.3%, without any extra latency. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1. Development of a Modality-Invariant Multi-Layer Perceptron to Predict Operational Events in Motor-Manual Willow Felling Operations

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