Unmanned aerial vehicle and artificial intelligence revolutionizing efficient and precision sustainable forest management

Liu, Tiedong; Sun, Yuxin; Cai, Wei; Zhang, Yangyang; Qiu, Zixuan; Gong, Wenfeng; Lei, Shuhan; Tong, Xili; Duan, Xuanyu

doi:10.1016/j.jclepro.2021.127546

Cited by 32 publications

(13 citation statements)

References 62 publications

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“…Topic 7- “Environment and Ecological Management” includes studies concerning the application of soft computing in the environment and ecological-related problems. There are diverse applications ranging from predicting harmful algal blooms using deep learning (Lee et al ., 2022), predicting water quality using deep neural networks (DNNs) (El Bilali et al ., 2022), estimation of carbon monoxide using deep forest algorithms (Wang et al ., 2022a, b, c, d), irrigation water resource management optimization (Wang et al ., 2022a, b, c, d), sustainable forest management using AI (Liu et al ., 2021a, b) and controlling energy and electronic waste generation in the cryptocurrencies mining (Jana et al ., 2022) and many other.…”

Section: Resultsmentioning

confidence: 99%

Soft computing in business: exploring current research and outlining future research directions

Singh

Koohang

et al. 2023

IMDS

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PurposeThe primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.Design/methodology/approachThis research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.FindingsThis study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.Practical implicationsThis analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.Originality/valueThis study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

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Section: Resultsmentioning

confidence: 99%

Soft computing in business: exploring current research and outlining future research directions

Singh

Koohang

et al. 2023

IMDS

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“…The SfM 3D point cloud reconstruction and measurement process was carried out on the original UAV image using the DJI Terra software to obtain the LVV data of areca in the research area. The steps of the SfM algorithm are as follows: (1) homonymous feature extraction and matching of dense images; (2) using the feature points of the same name for image relative orientation and calculating the external orientation elements; (3) generation of sparse point cloud by aerotriangulation and iterative bundle adjustment; (4) a dense point cloud is obtained based on sparse point cloud encryption [14]. The digital orthophoto map (DOM), digital surface model (DSM), and areca 3D point cloud models in the research area were obtained following SfM 3D point cloud reconstruction.…”

Section: Multispectral Vegetation Index Index Description Formulamentioning

confidence: 99%

Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors

et al. 2021

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Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). However, the correlation between the severity of the yellow leaf disease of arecanut and LVV must be demonstrated through research. Therefore, this study aims to apply the multispectral data obtained by the UAV along with the high-resolution UAV remote sensing images to obtain five vegetation indexes such as the normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), leaf chlorophyll index (LCI), green normalized difference vegetation index (GNDVI), and normalized difference red edge (NDRE) index, and establish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut, which is expressed by the proportion of the yellowing area of a single areca crown (in percentage). The traditional qualitative expression of this disease is transformed into the quantitative expression of the yellow leaf disease of arecanut per plant. The results demonstrate that the classification accuracy of the test set of the BPNN algorithm and SVM algorithm is the highest, at 86.57% and 86.30%, respectively. Additionally, the UAV structure from motion technology is used to measure the LVV of a single areca tree and establish a model of the correlation between the LVV and the severity of the yellow leaf disease of arecanut. The results show that the relative root mean square error is between 34.763% and 39.324%. This study presents the novel quantitative expression of the severity of the yellow leaf disease of arecanut, along with the correlation between the LVV of areca and the severity of the yellow leaf disease of arecanut. Significant development is expected in the degree of integration of multispectral software and hardware, observation accuracy, and ease of use of UAVs owing to the rapid progress of spectral sensing technology and the image processing and analysis algorithms.

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“…As stated in Almaki et al [2], the fourth industrial revolution (4IR) is bringing new opportunities to employ advanced technology to achieve an efficient and sustainable means of boosting crop productivity. For instance, unmanned aerial vehicles (UAVs) open new paths that advance the solution of existing problems, such as for package delivery in logistics [3], in the field of agriculture 4.0 [4], Industry 4.0 [5], artificial intelligence [6], the Internet of Things (IoT) [7], remote sensing [8] and many others. In precision agriculture, UAVs are widely used to map the spatial and temporal variability of various parameters in the cultivation environment, helping producers in decision-making and consequently improving agricultural management [9,10].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

et al. 2023

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Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot’s operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects.

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Unmanned aerial vehicle and artificial intelligence revolutionizing efficient and precision sustainable forest management

Cited by 32 publications

References 62 publications

Soft computing in business: exploring current research and outlining future research directions

Soft computing in business: exploring current research and outlining future research directions

Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors

Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

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