Tree Crown Detection, Delineation and Counting in UAV Remote Sensed Images: A Neural Network Based Spectral–Spatial Method

Kestur, Ramesh; Angural, Akanksha; Bashir, Bazila; Omkar, S. N.; Anand, G. V.; Meenavathi, M. B.

doi:10.1007/s12524-018-0756-4

Cited by 32 publications

(26 citation statements)

References 20 publications

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“…Another investigation showed the potential of UAV-based tree crown delineation in small holder field using aerial dataset from RGB cameras. The experimental subjects were banana, mango and coconut (Kestur et al 2018). In some multi-story cropping pattern, like a cultivation system mixed with banana, orange and bamboo, crops discrimination has been tested, depending on diverse spectral response and crop height.…”

Section: Biophysical and Geometrical Parameters Measurementmentioning

confidence: 99%

“…In the case of segmenting connected crowns, watershed algorithm had a good performance using images marked by distance transform. The neural network classifier is a single hidden layer feed forward one (Kestur et al 2018). Regarded DSM as the inputs, a combination of local maxima cues information and orientation symmetry performed better in the final transform (Ok Ozdarici-Ok, 2018b).…”

Section: Biophysical and Geometrical Parameters Measurementmentioning

confidence: 99%

See 1 more Smart Citation

Opportunities of Uavs in Orchard Management

Zhang

Valente

Kooistra

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The growth process of fruit trees is accompanied by a large number of monitoring and management activities, such as pruning, irrigation, fertilization, spraying, and harvesting, which are labour intensive and time consuming. In the context of precision agriculture, automation and precision orchard management not only saves labour resources and increases the income of growers, but also has great significance in improving resource utilization. Recent technological developments enable Unmanned Aerial Vehicles (UAVs, also commonly referred to as Unmanned Aerial Systems, or ‘drones’) to become an efficient monitoring tool for improving orchard management, that can provide growers much more detailed and precise information about fruit crops health status, geometric variables, physiological variables etc. This paper reviews the use of UAVs in orchard management, with a focus on recent UAV applications, synthetically describing the existing situation (e.g., general data processing approaches, sensing platform and sensor uploaded). The challenges and prospects of UAVs opportunities in orchard management are also summarized.</p>

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Section: Biophysical and Geometrical Parameters Measurementmentioning

confidence: 99%

Section: Biophysical and Geometrical Parameters Measurementmentioning

confidence: 99%

Opportunities of Uavs in Orchard Management

Zhang

Valente

Kooistra

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Initial captures were transformed into an HSV colour representation, and then binarized and conveniently cropped in sub-images, with which the CNN classifier was trained. In Kestur et al [22], an ELM methodology was proposed for detecting tree crowns from aerial images captured in the visible spectrum. Thus, the developed ELM spectral classifier was applied in order to segment the tree crowns-pixel areas from the rest of the image.…”

Section: Introductionmentioning

confidence: 99%

Automated Identification of Crop Tree Crowns from UAV Multispectral Imagery by Means of Morphological Image Analysis

et al. 2020

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Within the context of precision agriculture, goods insurance, public subsidies, fire damage assessment, etc., accurate knowledge about the plant population in crops represents valuable information. In this regard, the use of Unmanned Aerial Vehicles (UAVs) has proliferated as an alternative to traditional plant counting methods, which are laborious, time demanding and prone to human error. Hence, a methodology for the automated detection, geolocation and counting of crop trees in intensive cultivation orchards from high resolution multispectral images, acquired by UAV-based aerial imaging, is proposed. After image acquisition, the captures are processed by means of photogrammetry to yield a 3D point cloud-based representation of the study plot. To exploit the elevation information contained in it and eventually identify the plants, the cloud is deterministically interpolated, and subsequently transformed into a greyscale image. This image is processed, by using mathematical morphology techniques, in such a way that the absolute height of the trees with respect to their local surroundings is exploited to segment the tree pixel-regions, by global statistical thresholding binarization. This approach makes the segmentation process robust against surfaces with elevation variations of any magnitude, or to possible distracting artefacts with heights lower than expected. Finally, the segmented image is analysed by means of an ad-hoc moment representation-based algorithm to estimate the location of the trees. The methodology was tested in an intensive olive orchard of 17.5 ha, with a population of 3919 trees. Because of the plot’s plant density and tree spacing pattern, typical of intensive plantations, many occurrences of intra-row tree aggregations were observed, increasing the complexity of the scenario under study. Notwithstanding, it was achieved a precision of 99.92%, a sensibility of 99.67% and an F-score of 99.75%, thus correctly identifying and geolocating 3906 plants. The generated 3D point cloud reported root-mean square errors (RMSE) in the X, Y and Z directions of 0.73 m, 0.39 m and 1.20 m, respectively. These results support the viability and robustness of this methodology as a phenotyping solution for the automated plant counting and geolocation in olive orchards.

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“…Maciel et al [23] detected the center line of citrus trees in a high-density orchard with a CNN algorithm in a sliding window and segmented single trees with a CNN; the results showed an overall accuracy of 94% in seven different test orchards. Ramesh et al [24] segmented single fruit trees with an extreme learning machine, a geometry filtering threshold, and a watershed separation algorithm and detected the numbers of banana, mango, and coconut trees in different orchards with high-resolution RGB cameras onboard a fixed-wing UAV and a multi-rotor UAV with an accuracy of 85%. Lin et al [25] segmented the areas of single-tree canopies in oblique UAV images with k-means clustering in the La*b* color space (defined by the International Commission on Illumination (CIE) in 1976) and a threshold method with pseudo-NDVI color mapping and texture mapping technology.…”

Section: Introductionmentioning

confidence: 99%

Citrus Tree Segmentation from UAV Images Based on Monocular Machine Vision in a Natural Orchard Environment

Chen

Hou

Tang

et al. 2019

Sensors

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The segmentation of citrus trees in a natural orchard environment is a key technology for achieving the fully autonomous operation of agricultural unmanned aerial vehicles (UAVs). Therefore, a tree segmentation method based on monocular machine vision technology and a support vector machine (SVM) algorithm are proposed in this paper to segment citrus trees precisely under different brightness and weed coverage conditions. To reduce the sensitivity to environmental brightness, a selective illumination histogram equalization method was developed to compensate for the illumination, thereby improving the brightness contrast for the foreground without changing its hue and saturation. To accurately differentiate fruit trees from different weed coverage backgrounds, a chromatic aberration segmentation algorithm and the Otsu threshold method were combined to extract potential fruit tree regions. Then, 14 color features, five statistical texture features, and local binary pattern features of those regions were calculated to establish an SVM segmentation model. The proposed method was verified on a dataset with different brightness and weed coverage conditions, and the results show that the citrus tree segmentation accuracy reached 85.27% ± 9.43%; thus, the proposed method achieved better performance than two similar methods.

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Tree Crown Detection, Delineation and Counting in UAV Remote Sensed Images: A Neural Network Based Spectral–Spatial Method

Cited by 32 publications

References 20 publications

Opportunities of Uavs in Orchard Management

Opportunities of Uavs in Orchard Management

Automated Identification of Crop Tree Crowns from UAV Multispectral Imagery by Means of Morphological Image Analysis

Citrus Tree Segmentation from UAV Images Based on Monocular Machine Vision in a Natural Orchard Environment

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