Tree's detection &amp; health's assessment from ultra-high resolution UAV imagery and deep learning

Şandric, Ionuţ; Irimia, Radu; Petropoulos, George P.; Anand, Akash; Srivastava, Prashant K.; Pleșoianu, Alin; Faraslis, Ioannis; Stateras, Dimitrios; Kalivas, Dionissios

doi:10.1080/10106049.2022.2036824

Cited by 14 publications

(7 citation statements)

References 46 publications

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“…It correctly identified all trees without classifying other objects as trees or falsely detecting trees in areas with no presence of trees. In comparison, other similar studies where soil and vegetation colors were very similar also reported very high accuracy [7,[14][15][16]. This highlights the high precision exhibited by convolutional neural networks in such applications.…”

Section: Discussionsupporting

confidence: 53%

“…It is important to assess the accuracy of each method individually and to compare the accuracy of the results among them. The accuracy of the Detectron2 algorithm was evaluated in terms of its correct recognition of objects, based on three statistical indicators that were calculated [7,16].…”

Section: Calculation Of Accuracy For Each Methods and Methods Compari...mentioning

confidence: 99%

“…Many researchers have used convolutional neural networks for tree detection from RGB aerial photographs, aiming to assess the performance of those networks in applications involving a wide variety of tree species and developmental stages. Most of them have utilized the Mask R-CNN algorithm [7,[14][15][16]. Additionally, many researchers and practitioners have applied these techniques to precision agriculture applications, such as assessing tree health [16] and estimating biomass [15].…”

Section: Introductionmentioning

confidence: 99%

“…Most of them have utilized the Mask R-CNN algorithm [7,[14][15][16]. Additionally, many researchers and practitioners have applied these techniques to precision agriculture applications, such as assessing tree health [16] and estimating biomass [15]. The Mask R-CNN algorithm was preferred over others for object detection in remote sensing products due to its additional capability of object segmentation by creating masks, thereby providing precise object boundaries.…”

Section: Introductionmentioning

confidence: 99%

“…The use of UAV systems for forestry and/or crop health using machine learning techniques for tree detection has been previously conducted by various researchers. For instance, Safonova et al [15] and Sandric et al [16] utilized images acquired from UAV systems to study the health of cultivated trees.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Technique Based on Machine Learning for Detecting and Segmenting Trees in Very High Resolution Digital Images from Unmanned Aerial Vehicles

Kouvaras,

Petropoulos

2024

Drones

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The present study proposes a technique for automated tree crown detection and segmentation in digital images derived from unmanned aerial vehicles (UAVs) using a machine learning (ML) algorithm named Detectron2. The technique, which was developed in the python programming language, receives as input images with object boundary information. After training on sets of data, it is able to set its own object boundaries. In the present study, the algorithm was trained for tree crown detection and segmentation. The test bed consisted of UAV imagery of an agricultural field of tangerine trees in the city of Palermo in Sicily, Italy. The algorithm’s output was the accurate boundary of each tree. The output from the developed algorithm was compared against the results of tree boundary segmentation generated by the Support Vector Machine (SVM) supervised classifier, which has proven to be a very promising object segmentation method. The results from the two methods were compared with the most accurate yet time-consuming method, direct digitalization. For accuracy assessment purposes, the detected area efficiency, skipped area rate, and false area rate were estimated for both methods. The results showed that the Detectron2 algorithm is more efficient in segmenting the relevant data when compared to the SVM model in two out of the three indices. Specifically, the Detectron2 algorithm exhibited a 0.959% and 0.041% fidelity rate on the common detected and skipped area rate, respectively, when compared with the digitalization method. The SVM exhibited 0.902% and 0.097%, respectively. On the other hand, the SVM classification generated better false detected area results, with 0.035% accuracy, compared to the Detectron2 algorithm’s 0.056%. Having an accurate estimation of the tree boundaries from the Detectron2 algorithm, the tree health assessment was evaluated last. For this to happen, three different vegetation indices were produced (NDVI, GLI and VARI). All those indices showed tree health as average. All in all, the results demonstrated the ability of the technique to detect and segment trees from UAV imagery.

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Section: Discussionsupporting

confidence: 53%

Section: Calculation Of Accuracy For Each Methods and Methods Compari...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Technique Based on Machine Learning for Detecting and Segmenting Trees in Very High Resolution Digital Images from Unmanned Aerial Vehicles

Kouvaras,

Petropoulos

2024

Drones

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Using high-resolution UAV imagery and artificial intelligence to detect and map landslide cracks automatically

Sandric,

Chitu,

Ilinca

et al. 2024

Landslides

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Mapping landslide cracks is essential in understanding landslide dynamics and evolution across space and time. Each landslide crack’s depth, direction, and width are related to the stress and strain imposed on the landslide body. Moreover, their spatial distribution can indicate areas where the landslide can extend, mainly if located in the upper part of the main landslide scarp. Even though the cracks leave a distinct pattern on the landslide body when fresh or when there is a high contrast between the bare soil and surrounding vegetation, these patterns gradually diminish over time, making their detection difficult. The problem of landslide cracks mapping in various environmental conditions and having different ages was tackled in the current study using very high spatial resolution unmanned aerial vehicle (UAV) aerial imagery and derived products in conjunction with deep learning models. U-Net and DeepLab CNN models were applied using masked and non-masked training samples with different tile sizes. As the tile size decreases, the performance metrics, such as precision, recall, and F1-score, generally decrease. Overall, the lowest accuracy was approximately 0.79 for non-mask samples and tile size of 64 pixels, and reached over 0.93 for masked samples and tile size of 512 pixels.

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Tree sapling vitality and recovery following the unprecedented 2018 drought in central Europe

Beloiu Schwenke,

Schönlau,

Beierkuhnlein

2023

Forest Ecosystems

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Tree's detection & health's assessment from ultra-high resolution UAV imagery and deep learning

Cited by 14 publications

References 46 publications

A Novel Technique Based on Machine Learning for Detecting and Segmenting Trees in Very High Resolution Digital Images from Unmanned Aerial Vehicles

A Novel Technique Based on Machine Learning for Detecting and Segmenting Trees in Very High Resolution Digital Images from Unmanned Aerial Vehicles

Using high-resolution UAV imagery and artificial intelligence to detect and map landslide cracks automatically

Tree sapling vitality and recovery following the unprecedented 2018 drought in central Europe

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