Correction: Nguyen et al. Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning. Remote Sens. 2021, 13, 260

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

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A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring

Sensors ◽

10.3390/s18113921 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3921 ◽

Cited By ~ 17

Author(s):

Wuttichai Boonpook ◽

Yumin Tan ◽

Yinghua Ye ◽

Peerapong Torteeka ◽

Kritanai Torsri ◽

...

Keyword(s):

Deep Learning ◽

Network Architecture ◽

Semantic Segmentation ◽

Water Levels ◽

Building Detection ◽

Network Training ◽

Building Information ◽

Open Standard ◽

Uav Images ◽

Uav Image

Buildings along riverbanks are likely to be affected by rising water levels, therefore the acquisition of accurate building information has great importance not only for riverbank environmental protection but also for dealing with emergency cases like flooding. UAV-based photographs are flexible and cloud-free compared to satellite images and can provide very high-resolution images up to centimeter level, while there exist great challenges in quickly and accurately detecting and extracting building from UAV images because there are usually too many details and distortions on UAV images. In this paper, a deep learning (DL)-based approach is proposed for more accurately extracting building information, in which the network architecture, SegNet, is used in the semantic segmentation after the network training on a completely labeled UAV image dataset covering multi-dimension urban settlement appearances along a riverbank area in Chongqing. The experiment results show that an excellent performance has been obtained in the detection of buildings from untrained locations with an average overall accuracy more than 90%. To verify the generality and advantage of the proposed method, the procedure is further evaluated by training and testing with another two open standard datasets which have a variety of building patterns and styles, and the final overall accuracies of building extraction are more than 93% and 95%, respectively.

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DEEP LEARNING FOR MONOCULAR DEPTH ESTIMATION FROM UAV IMAGES

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-451-2020 ◽

2020 ◽

Vol V-2-2020 ◽

pp. 451-458

Author(s):

L. Madhuanand ◽

F. Nex ◽

M. Y. Yang

Keyword(s):

Deep Learning ◽

Ground Level ◽

Depth Estimation ◽

Aerial Images ◽

Aerial Image ◽

Depth Information ◽

Single Image ◽

Monocular Depth ◽

Uav Images ◽

Image Depth

Abstract. Depth is an essential component for various scene understanding tasks and for reconstructing the 3D geometry of the scene. Estimating depth from stereo images requires multiple views of the same scene to be captured which is often not possible when exploring new environments with a UAV. To overcome this monocular depth estimation has been a topic of interest with the recent advancements in computer vision and deep learning techniques. This research has been widely focused on indoor scenes or outdoor scenes captured at ground level. Single image depth estimation from aerial images has been limited due to additional complexities arising from increased camera distance, wider area coverage with lots of occlusions. A new aerial image dataset is prepared specifically for this purpose combining Unmanned Aerial Vehicles (UAV) images covering different regions, features and point of views. The single image depth estimation is based on image reconstruction techniques which uses stereo images for learning to estimate depth from single images. Among the various available models for ground-level single image depth estimation, two models, 1) a Convolutional Neural Network (CNN) and 2) a Generative Adversarial model (GAN) are used to learn depth from aerial images from UAVs. These models generate pixel-wise disparity images which could be converted into depth information. The generated disparity maps from these models are evaluated for its internal quality using various error metrics. The results show higher disparity ranges with smoother images generated by CNN model and sharper images with lesser disparity range generated by GAN model. The produced disparity images are converted to depth information and compared with point clouds obtained using Pix4D. It is found that the CNN model performs better than GAN and produces depth similar to that of Pix4D. This comparison helps in streamlining the efforts to produce depth from a single aerial image.

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Automatic Detection of Oil Palm Tree from UAV Images Based on the Deep Learning Method

Applied Artificial Intelligence ◽

10.1080/08839514.2020.1831226 ◽

2020 ◽

Vol 35 (1) ◽

pp. 13-24

Author(s):

Xinni Liu ◽

Kamarul Hawari Ghazali ◽

Fengrong Han ◽

Izzeldin Ibrahim Mohamed

Keyword(s):

Deep Learning ◽

Oil Palm ◽

Automatic Detection ◽

Palm Tree ◽

Learning Method ◽

Uav Images

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Insect Damaged Tree Detection with Drone Data and Deep Learning Technique, Case Study: Abies Mariesii Forest, Zao Mountain, Japan

10.5194/egusphere-egu2020-17917 ◽

2020 ◽

Author(s):

Nguyen Ha Trang ◽

Yago Diez ◽

Larry Lopez

Keyword(s):

Deep Learning ◽

Bark Beetles ◽

Forest Cover ◽

Learning Algorithm ◽

Mixed Forest ◽

Flying Height ◽

Mountainous Area ◽

Heterogeneous Distribution ◽

Tree Detection ◽

Abies Mariesii

The outbreak of fir bark beetles (Polygraphus proximus Blandford) in natural Abies Mariesii forest on Zao Mountain were reported in 2016. With the recent development of deep learning and drones, it is possible to automatically detect trees in both man-made and natural forests including damaged tree detection. However there are still some challenges in using deep learning and drones for sick tree detection in mountainous area that we want to address: (i) mixed forest structure with overlapping canopies, (ii) heterogeneous distribution of species in different sites, (iii) high slope of mountainous area and (iv) variation of mountainous climate condition. The current work can be summarized into three stages: data collection, data preparation and data processing. All the data were collected by DJI Mavic 2 pro at 60-70m flying height from the take off point with ground sampling distance (GSD) are ranging from1.23 cm to 2.54 cm depending on the slope of the sites. To prepare the data to be processed using a Convolutional Neural Network (CNN), all images were stitched together using Agisoft&#8217;s metashape software to create five orthomosaics of five study sites. Every site has different percentage of fir according to the change of elevation. We then manually annotated all the mosaics with GIMP to categorize all the forest cover into 6 classes: dead fir, sick fir, healthy fir, deciduous trees, grass and uncovered (pathway, building and soil). The mosaics are automatically divided into small patches with the assigned categories by our algorithm with first trial window size of 200 pixel x 200 pixel, which we temporally see can cover the medium fir trees. We will also try different window sizes and evaluate how this parameter affects results. The resulting patches were finally used as the input for CNN architecture to detect the damaged trees. The work is still on going and we expect to achieve the results with high classification accuracy in terms of deep learning algorithm allowing us to build maps regarding health status of all fir trees.&#160;Keywords: Deep learning, CNN, drones, UAVs, tree detection, sick trees, insect damaged trees, forest&#160;

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A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images

Sensors ◽

10.3390/s21196540 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6540

Author(s):

Qian Pan ◽

Maofang Gao ◽

Pingbo Wu ◽

Jingwen Yan ◽

Shilei Li

Keyword(s):

Deep Learning ◽

Unmanned Aerial Vehicle ◽

Recognition Accuracy ◽

Yellow Rust ◽

Semantic Segmentation ◽

High Accuracy ◽

Economic Losses ◽

Support Vector ◽

Aerial Vehicle ◽

Uav Images

Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identifying wheat yellow rust from unmanned aerial vehicle (UAV) images. The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate the spatial generalization of the model. In addition, it was proposed to use the high-accuracy classification results of traditional algorithms as weak samples for wheat yellow rust identification. The recognition accuracy of the PSPNet model in this study reached 98%. On this basis, this study used the trained semantic segmentation model to recognize another wheat field. The results showed that the method had certain generalization ability, and its accuracy reached 98%. In addition, the high-accuracy classification result of a support vector machine was used as a weak label by weak supervision, which better solved the labeling problem of large-size images, and the final recognition accuracy reached 94%. Therefore, the present study method facilitated timely control measures to reduce economic losses.

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INDIVIDUAL BANANA TREE CROWN DELINEATION USING UNMANNED AERIAL VEHICLE (UAV) IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2021-581-2021 ◽

2021 ◽

Vol XLIII-B3-2021 ◽

pp. 581-585

Author(s):

S. Kuikel ◽

B. Upadhyay ◽

D. Aryal ◽

S. Bista ◽

B. Awasthi ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Tree Crown ◽

Individual Tree ◽

Banana Tree ◽

Crown Delineation ◽

Uav Images

Abstract. Individual Tree Crown (ITC) delineation from aerial imageries plays an important role in forestry management and precision farming. Several conventional as well as machine learning and deep learning algorithms have been recently used in ITC detection purpose. In this paper, we present Convolutional Neural Network (CNN) and Support Vector Machine (SVM) as the deep learning and machine learning algorithms along with conventional methods of classification such as Object Based Image Analysis (OBIA) and Nearest Neighborhood (NN) classification for banana tree delineation. The comparison was done based by considering two cases; Firstly, every single classifier was compared by feeding the image with height information to see the effect of height in banana tree delineation. Secondly, individual classifiers were compared quantitatively and qualitatively based on five metrices i.e., Overall Accuracy, Recall, Precision, F-Score, and Intersection Over Union (IoU) and best classifier was determined. The result shows that there are no significant differences in the metrices when height information was fed as there were banana tree of almost similar height in the farm. The result as discussed in quantitative and qualitative analysis showed that the CNN algorithm out performed SVM, OBIA and NN techniques for crown delineation in term of performance measures.

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A Study on the Detection of Cattle in UAV Images Using Deep Learning

10.20944/preprints201912.0089.v1 ◽

2019 ◽

Author(s):

Jayme Garcia Arnal Barbedo ◽

Luciano Vieira Koenigkan ◽

Thiago Teixeira Santos ◽

Patrícia Menezes Santos

Keyword(s):

Deep Learning ◽

Bos Taurus ◽

Relevant Information ◽

Learning Technologies ◽

Aerial Images ◽

Ground Sample ◽

Bos Taurus Indicus ◽

Uav Images ◽

Production Areas ◽

Animal Detection

Unmanned Aerial Vehicles (UAVs) are being increasingly viewed as valuable tools to aid the management of farms. This kind of technology can be particularly useful in the context of extensive cattle farming, as production areas tend to be expansive and animals tend to be more loosely monitored. With the advent of deep learning, and Convolutional Neural Networks (CNNs) in particular, extracting relevant information from aerial images has become more effective. Despite the technological advancements in drone, imaging and machine learning technologies, the application of UAVs for cattle monitoring is far from being thoroughly studied, with many research gaps still remaining. In this context, the objectives of this study were threefold: 1) to determine the highest possible accuracy that could be achieved in the detection of animals of the Canchim breed, which is visually similar to the Nelore breed (\textit{Bos taurus indicus}); 2) to determine the ideal Ground Sample Distance (GSD) for animal detection; 3) to determine the most accurate CNN architecture for this specific problem. The experiments involved 1,853 images containing 8,629 samples of animals, and 15 different CNN architectures were tested. A total of 900 models were trained (15 CNN architectures * 3 spacial resolutions * 2 datasets * 10-fold cross validation), allowing for a deep analysis of the several aspects that impact the detection of cattle using aerial images captured using UAVs. Results revealed that many CNN architectures are robust enough to reliably detect animals in aerial images even under far from ideal conditions, indicating the viability of using UAVs for cattle monitoring.

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A Study on the Detection of Cattle in UAV Images Using Deep Learning

Sensors ◽

10.3390/s19245436 ◽

2019 ◽

Vol 19 (24) ◽

pp. 5436 ◽

Cited By ~ 7

Author(s):

Jayme Garcia Arnal Barbedo ◽

Luciano Vieira Koenigkan ◽

Thiago Teixeira Santos ◽

Patrícia Menezes Santos

Keyword(s):

Deep Learning ◽

Bos Taurus ◽

Relevant Information ◽

Learning Technologies ◽

Aerial Images ◽

Ground Sample ◽

Bos Taurus Indicus ◽

Uav Images ◽

Production Areas ◽

Animal Detection

Unmanned aerial vehicles (UAVs) are being increasingly viewed as valuable tools to aid the management of farms. This kind of technology can be particularly useful in the context of extensive cattle farming, as production areas tend to be expansive and animals tend to be more loosely monitored. With the advent of deep learning, and convolutional neural networks (CNNs) in particular, extracting relevant information from aerial images has become more effective. Despite the technological advancements in drone, imaging and machine learning technologies, the application of UAVs for cattle monitoring is far from being thoroughly studied, with many research gaps still remaining. In this context, the objectives of this study were threefold: (1) to determine the highest possible accuracy that could be achieved in the detection of animals of the Canchim breed, which is visually similar to the Nelore breed (Bos taurus indicus); (2) to determine the ideal ground sample distance (GSD) for animal detection; (3) to determine the most accurate CNN architecture for this specific problem. The experiments involved 1853 images containing 8629 samples of animals, and 15 different CNN architectures were tested. A total of 900 models were trained (15 CNN architectures × 3 spacial resolutions × 2 datasets × 10-fold cross validation), allowing for a deep analysis of the several aspects that impact the detection of cattle using aerial images captured using UAVs. Results revealed that many CNN architectures are robust enough to reliably detect animals in aerial images even under far from ideal conditions, indicating the viability of using UAVs for cattle monitoring.

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