Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning

Nguyen, Ha-Thanh; Caceres, Maximo Larry Lopez; Moritake, Koma; Kentsch, Sarah; Hase, S.; Diéz, Yago

doi:10.3390/rs13020260

Cited by 36 publications

(41 citation statements)

References 32 publications

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“…The mapping and detection of individual tree crowns, tree/plant/vegetation species, crops, and wetlands from UAV-based images are achieved by diverse CNN architectures, which are used to perform different tasks, including path-based classification [78][79][80][81][82][83][84][85][86][87], object detection [88][89][90][91][92][93][94][95][96][97], and semantic segmentation [98][99][100][101][102][103][104][105][106][107]. Recently, semantic segmentation, a commonly used term in computer vision where each pixel within the input imagery is assigned to a particular class, has been a widely used technique in diverse earth-related applications [108].…”

Section: Related Workmentioning

confidence: 99%

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

et al. 2021

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Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.

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Section: Related Workmentioning

confidence: 99%

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

et al. 2021

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“…The proposed method attained better recognition performance than several well-known methods, such as support vector machines, AlexNet, VGG, and Inception-V3. Another work where UAVs were used to capture aerial images for the further identification of sick trees was proposed in [6]. In this work, the authors wanted to detect sick fir trees, and for that, they started by obtaining a Digital Surface Model (DSM) from the aerial images, on top of which an algorithm developed by them was run to detect treetops.…”

Section: Vision-based Perceptionmentioning

confidence: 99%

“…Health and diseases [5] Disease detection Unimodal UAV [6] Disease detection Unimodal UAV Inventory and structure [11] Inventory characterisation Unimodal Handheld [38] Biomass parameters Unimodal Airborne [93] Biomass estimation Multimodal Spaceborne Navigation [28] Autonomous flight Unimodal UAV [74] Autonomous rubber-tapping Unimodal Caterpillar robot [97] Autonomous navigation Multimodal Quadruped robot…”

Section: Category Work Objective Perception Type Platformsmentioning

confidence: 99%

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Unimodal and Multimodal Perception for Forest Management: Review and Dataset

et al. 2021

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Robotics navigation and perception for forest management are challenging due to the existence of many obstacles to detect and avoid and the sharp illumination changes. Advanced perception systems are needed because they can enable the development of robotic and machinery solutions to accomplish a smarter, more precise, and sustainable forestry. This article presents a state-of-the-art review about unimodal and multimodal perception in forests, detailing the current developed work about perception using a single type of sensors (unimodal) and by combining data from different kinds of sensors (multimodal). This work also makes a comparison between existing perception datasets in the literature and presents a new multimodal dataset, composed by images and laser scanning data, as a contribution for this research field. Lastly, a critical analysis of the works collected is conducted by identifying strengths and research trends in this domain.

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“…Notably, some noteworthy work has been performed to solve these setbacks. On the subject of disease detection and recognition, the authors in [10] develop an algorithm to automatically detect and subsequently classify insect-infected fir trees (Abies mariesii) and deciduous trees. Their methodology is based on treetop (aerial) photos from UAVs.…”

Section: Introductionmentioning

confidence: 99%

Detection and Characterization of Stressed Sweet Cherry Tissues Using Machine Learning

Chaschatzis

Karaiskou

Mouratidis

et al. 2021

Drones

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Recent technological developments in the primary sector and machine learning algorithms allow the combined application of many promising solutions in precision agriculture. For example, the YOLOv5 (You Only Look Once) and ResNet Deep Learning architecture provide high-precision real-time identifications of objects. The advent of datasets from different perspectives provides multiple benefits, such as spheric view of objects, increased information, and inference results from multiple objects detection per image. However, it also raises crucial obstacles such as total identifications (ground truths) and processing concerns that can lead to devastating consequences, including false-positive detections with other erroneous conclusions or even the inability to extract results. This paper introduces experimental results from the machine learning algorithm (Yolov5) on a novel dataset based on perennial fruit crops, such as sweet cherries, aiming to enhance precision agriculture resiliency. Detection is oriented on two points of interest: (a) Infected leaves and (b) Infected branches. It is noteworthy that infected leaves or branches indicate stress, which may be due to either a stress/disease (e.g., Armillaria for sweet cherries trees, etc.) or other factors (e.g., water shortage, etc). Correspondingly, the foliage of a tree shows symptoms, while this indicates the stages of the disease.

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Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning

Cited by 36 publications

References 32 publications

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

Unimodal and Multimodal Perception for Forest Management: Review and Dataset

Detection and Characterization of Stressed Sweet Cherry Tissues Using Machine Learning

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