UAV remote sensing monitoring of pine forest diseases based on improved Mask R-CNN

Hu, Gensheng; Wang, Tongxiang; Wan, Mingzhu; Bao, Wenxia; Zeng, Weihui

doi:10.1080/01431161.2022.2032455

Cited by 22 publications

(5 citation statements)

References 35 publications

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“…In the field of computer vision, exactly segmenting individual objects of interest from an image is known as instance segmentation . The Mask R‐CNN algorithm has shown promise in tree crown identification and delineation in plantations (Hao et al., 2021; Kunyong et al., 2022), pine forests (Gensheng et al., 2022; Ocer et al., 2020), urban woodlands (Ocer et al., 2020) and forest fragments (Braga et al., 2020). Mask R‐CNN has features that could allow it to overcome the challenges of delineating crowns in complex tropical canopies by discriminating based on the spectral and textural signals which are rich due to the phylogenetic diversity.…”

Section: Introductionmentioning

confidence: 99%

Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R‐CNN

Ball

Hickman

Jackson

et al. 2023

Remote Sens Ecol Conserv

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Tropical forests are a major component of the global carbon cycle and home to two‐thirds of terrestrial species. Upper‐canopy trees store the majority of forest carbon and can be vulnerable to drought events and storms. Monitoring their growth and mortality is essential to understanding forest resilience to climate change, but in the context of forest carbon storage, large trees are underrepresented in traditional field surveys, so estimates are poorly constrained. Aerial photographs provide spectral and textural information to discriminate between tree crowns in diverse, complex tropical canopies, potentially opening the door to landscape monitoring of large trees. Here we describe a new deep convolutional neural network method, Detectree2, which builds on the Mask R‐CNN computer vision framework to recognize the irregular edges of individual tree crowns from airborne RGB imagery. We trained and evaluated this model with 3797 manually delineated tree crowns at three sites in Malaysian Borneo and one site in French Guiana. As an example application, we combined the delineations with repeat lidar surveys (taken between 3 and 6 years apart) of the four sites to estimate the growth and mortality of upper‐canopy trees. Detectree2 delineated 65 000 upper‐canopy trees across 14 km2 of aerial images. The skill of the automatic method in delineating unseen test trees was good (F1 score = 0.64) and for the tallest category of trees was excellent (F1 score = 0.74). As predicted from previous field studies, we found that growth rate declined with tree height and tall trees had higher mortality rates than intermediate‐size trees. Our approach demonstrates that deep learning methods can automatically segment trees in widely accessible RGB imagery. This tool (provided as an open‐source Python package) has many potential applications in forest ecology and conservation, from estimating carbon stocks to monitoring forest phenology and restoration.Python package available to install at https://github.com/PatBall1/Detectree2.

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Section: Introductionmentioning

confidence: 99%

Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R‐CNN

Ball

Hickman

Jackson

et al. 2023

Remote Sens Ecol Conserv

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show abstract

“…In the research on UAV remote sensing image classification, the classification accuracy is further improved after using deep learning algorithms, such as using the improved deep learning model to identify sunflower lodging information [26], using deep learning models to classify crops, the classification models [27][28][29][30], using U-Net to segment the canopy of walnut trees [31], and using the deep learning semantic segmentation model to identify the canopy of kiwi fruit in the orchard [32]. At present, most researchers focus on exploring and improving the deep learning model to process remote sensing image classification tasks more efficiently, and exploit a new path for the field of UAV and other types of remote sensing image data processing [33], such as using a lightweight convolutional neural network structure to classify tree species, which solves the problem of limited monitoring of forest land by satellite images [34], and using the improved U-Net model to classify crops, which has significantly improved the classification accuracy compared to the original U-Net model [35].…”

Section: Introductionmentioning

confidence: 99%

Economic Fruit Forest Classification Based on Improved U-Net Model in UAV Multispectral Imagery

Jia

Yang

et al. 2023

Remote Sensing

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Economic fruit forest is an important part of Chinese agriculture with high economic value and ecological benefits. Using UAV multi-spectral images to research the classification of economic fruit forests based on deep learning is of great significance for accurately understanding the distribution and scale of fruit forests and the status quo of national economic fruit forest resources. Based on the multi-spectral remote sensing images of UAV, this paper constructed semantic segmentation data of economic fruit forests, conducted a comparative study on the classification and identification of economic fruit forests of FCN, SegNet, and U-Net classic semantic segmentation models, and proposed an improved ISDU-Net model. The recognition accuracies of the ISDU-Net were 87.73%, 70.68%, 78.69%, and 0.84, respectively, in terms of pixel accuracy, average intersection ratio, frequency weight intersection ratio, and Kappa coefficient, which were 3.19%, 8.90%, and 4.51% higher than the original U-Net model. The results showed that the improved ISDU-Net could effectively improve the learning ability of the model, perform better in the prediction of short sample categories, obtain a higher classification accuracy of fruit forest crops, and provide a new idea for the research on accurate fruit forest identification.

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“…Hu added a multiscale receptive field block module on the Mask R-CNN model to monitor pine diseases in forests. The precision, recall, and F1-score increased by 22.4%, 3.5% and 14.4% respectively [39]. Li used a cycle generative adversarial network (GAN) [40] to augment the wood defect dataset and constructed a layered deformable Mask R-CNN model to detect and segment the knots, cracks, and worms in Betula davuric, Pinus and Populus L species.…”

Section: Introductionmentioning

confidence: 99%

A Novel Wood Log Measurement Combined Mask R-CNN and Stereo Vision Camera

Sun

Cao³

et al. 2023

Forests

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Wood logs need to be measured for size when passing through customs to verify their quantity and volume. Due to the large number of wood logs needs through customs, a fast and accurate measurement method is required. The traditional log measurement methods are inefficient, have significant errors in determining the long and short diameters of the wood, and are difficult to achieve fast measurements in complex wood stacking environments. We use a Mask R-CNN instance segmentation model to detect the contour of the wood log and employ a binocular stereo camera to measure the log diameter. A rotation search algorithm centered on the wood contour is proposed to find long and short diameters and to optimal log size according to the Chinese standard. The experiments show that the Mask R-CNN we trained obtains 0.796 average precision and 0.943 IOUmask, and the recognition rate of wood log ends reaches 98.2%. The average error of the short diameter of the measurement results is 5.7 mm, the average error of the long diameter is 7.19 mm, and the average error of the diameter of the wood is 5.3 mm.

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UAV remote sensing monitoring of pine forest diseases based on improved Mask R-CNN

Cited by 22 publications

References 35 publications

Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R‐CNN

Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R‐CNN

Economic Fruit Forest Classification Based on Improved U-Net Model in UAV Multispectral Imagery

A Novel Wood Log Measurement Combined Mask R-CNN and Stereo Vision Camera

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