Detection of Citrus Trees From Uav DSMS

Ok, Ali Özgün; Ozdarici-Ok, Asli

doi:10.5194/isprs-annals-iv-1-w1-27-2017

Cited by 17 publications

(7 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Typically, the characterization of individual trees in orchards based on images from UAVs involves the estimation of tree numbers [26,27], tree height [28,29], and tree-crown parameters [21,29], which provide key indicators of plant growth [18] and plant yield [30] as well as for the assessment of pruning effects [24]. The most important parameters for the characterization of individual trees in orchards require the detection of individual trees and the delineation of individual tree-crowns.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard

Dong

Zhang

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

The extraction of information about individual trees is essential to supporting the growing of fruit in orchard management. Data acquired from spectral sensors mounted on unmanned aerial vehicles (UAVs) have very high spatial and temporal resolution. However, an efficient and reliable method for extracting information about individual trees with irregular tree-crown shapes and a complicated background is lacking. In this study, we developed and tested the performance of an approach, based on UAV imagery, to extracting information about individual trees in an orchard with a complicated background that includes apple trees (Plot 1) and pear trees (Plot 2). The workflow involves the construction of a digital orthophoto map (DOM), digital surface models (DSMs), and digital terrain models (DTMs) using the Structure from Motion (SfM) and Multi-View Stereo (MVS) approaches, as well as the calculation of the Excess Green minus Excess Red Index (ExGR) and the selection of various thresholds. Furthermore, a local-maxima filter method and marker-controlled watershed segmentation were used for the detection and delineation, respectively, of individual trees. The accuracy of the proposed method was evaluated by comparing its results with manual estimates of the numbers of trees and the areas and diameters of tree-crowns, all three of which parameters were obtained from the DOM. The results of the proposed method are in good agreement with these manual estimates: The F-scores for the estimated numbers of individual trees were 99.0% and 99.3% in Plot 1 and Plot 2, respectively, while the Producer’s Accuracy (PA) and User’s Accuracy (UA) for the delineation of individual tree-crowns were above 95% for both of the plots. For the area of individual tree-crowns, root-mean-square error (RMSE) values of 0.72 m2 and 0.48 m2 were obtained for Plot 1 and Plot 2, respectively, while for the diameter of individual tree-crowns, RMSE values of 0.39 m and 0.26 m were obtained for Plot 1 (339 trees correctly identified) and Plot 2 (203 trees correctly identified), respectively. Both the areas and diameters of individual tree-crowns were overestimated to varying degrees.

show abstract

Section: Introductionmentioning

confidence: 99%

Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard

Dong

Zhang

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Ultimately, using one or both depends on the tasks and the specific data to be obtained. In arboriculture, UAVs are a very practical tool presenting a medium-to-high performance for multiple applications [4][5][6][22][23][24][25][26]35,72]. With the high spatial and temporal resolutions that can be achieved, they allow the characterization of entire fields with high-quality images to analyze individual trees.…”

Section: Discussionmentioning

confidence: 99%

“…The diversity of platforms and sensors discussed above (See Figures 1 and 2) has motivated many research groups to explore and adopt UAVs into their agricultural practices in order to meet specific needs [22][23][24][25][26]. UAVs have found applicability in a variety of agricultural study areas, one of which is arboriculture, where there is growing interest in this segment of technology, particularly because of the high-resolution data that can be collected flexibly in a short time and for a relatively low price.…”

Section: Use Of Uav In Arboriculturementioning

confidence: 99%

See 1 more Smart Citation

Towards Smart Irrigation: A Literature Review on the Use of Geospatial Technologies and Machine Learning in the Management of Water Resources in Arboriculture

et al. 2022

View full text Add to dashboard Cite

Agriculture consumes an important ratio of the water reserve in irrigated areas. The improvement of irrigation is becoming essential to reduce this high water consumption by adapting supplies to the crop needs and avoiding losses. This global issue has prompted many scientists to reflect on sustainable solutions using innovative technologies, namely Unmanned Aerial Vehicles (UAV), Machine Learning (ML), and the Internet of Things (IoT). This article aims to present an overview of the use of these new technologies in the analysis of the water status of crops for better irrigation management, with an emphasis on arboriculture. The review demonstrated the importance of UAV-ML-IoT technologies. This contribution is due to the relevant information that can be collected from IoT sensors and extracted from UAV images through various sensors (RGB, multispectral, hyperspectral, thermal), and the ability of ML models to monitor and predict water status. The review in this paper is organized into four main sections: the use of UAV in arboriculture, UAV for irrigation management in arboriculture, IoT systems and irrigation management, and ML for data processing and decision-making. A discussion is presented regarding the prospects for smart irrigation using geospatial technologies and machine learning.

show abstract

“…It depends on the trees distribution in the orchard, the size of the canopies, and the images' resolution. In some cases, the trees do not overlap in the field, meaning they can be directly detected and segmented using object segmentation algorithms [13][14][15][16][17]. However, sometimes the trees overlap, making it difficult for the algorithms to segment each one individually.…”

Section: Introductionmentioning

confidence: 99%

Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms

2021

View full text Add to dashboard Cite

Smart agriculture is a new concept that combines agriculture and new technologies to improve the yield’s quality and quantity as well as facilitate many tasks for farmers in managing orchards. An essential factor in smart agriculture is tree crown segmentation, which helps farmers automatically monitor their orchards and get information about each tree. However, one of the main problems, in this case, is when the trees are close to each other, which means that it would be difficult for the algorithm to delineate the crowns correctly. This paper used satellite images and machine learning algorithms to segment and classify trees in overlapping orchards. The data used are images from the Moroccan Mohammed VI satellite, and the study region is the OUARGHA citrus orchard located in Morocco. Our approach starts by segmenting the rows inside the parcel and finding all the trees there, getting their canopies, and classifying them by size. In general, the model inputs the parcel’s image and other field measurements to classify the trees into three classes: missing/weak, normal, or big. Finally, the results are visualized in a map containing all the trees with their classes. For the results, we obtained a score of 0.93 F-measure in rows segmentation. Additionally, several field comparisons were performed to validate the classification, dozens of trees were compared and the results were very good. This paper aims to help farmers to quickly and automatically classify trees by crown size, even if there are overlapping orchards, in order to easily monitor each tree’s health and understand the tree’s distribution in the field.

show abstract

Detection of Citrus Trees From Uav DSMS

Cited by 17 publications

References 45 publications

Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard

Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard

Towards Smart Irrigation: A Literature Review on the Use of Geospatial Technologies and Machine Learning in the Management of Water Resources in Arboriculture

Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms

Contact Info

Product

Resources

About