scholarly journals Image Segmentation Parameter Selection and Ant Colony Optimization for Date Palm Tree Detection and Mapping from Very-High-Spatial-Resolution Aerial Imagery

2018 ◽  
Vol 10 (9) ◽  
pp. 1413 ◽  
Author(s):  
Rami Al-Ruzouq ◽  
Abdallah Shanableh ◽  
Mohamed Barakat A. Gibril ◽  
Saeed AL-Mansoori
Keyword(s):  
Feature Selection ◽  
Date Palm ◽  
Aerial Images ◽  
Support Vector ◽  
Palm Tree ◽  
Tree Detection ◽  
Palm Trees ◽  
Object Based ◽  
Accuracy Measure ◽  
Very High Spatial Resolution

Accurate mapping of date palm trees is essential for their sustainable management, yield estimation, and environmental studies. In this study, we integrated geographic object-based image analysis, class-specific accuracy measures, fractional factorial design, metaheuristic feature-selection technique, and rule-based classification to detect and map date palm trees from very-high-spatial-resolution (VHSR) aerial images of two study areas. First, multiresolution segmentation was optimized through the synergy of the F1-score accuracy measure and the robust Taguchi design. Second, ant colony optimization (ACO) was adopted to select the most significant features. Out of 31 features, only 12 significant color invariants and textural features were selected. Third, based on the selected features, the rule-based classification with the aid of a decision tree algorithm was applied to extract date palm trees. The proposed methodology was developed on a subset of the first study area, and ultimately applied to the second study area to investigate its efficiency and transferability. To evaluate the proposed classification scheme, various supervised object-based algorithms, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (k-NN), were applied to the first study area. The result of image segmentation optimization demonstrated that segmentation optimization based on an integrated F1-score class-specific accuracy measure and Taguchi statistical design showed improvement compared with objective function, along with the Taguchi design. Moreover, the result of the feature selection by ACO outperformed, with almost 88% overall accuracy, several feature-selection techniques, such as chi-square, correlation-based feature selection, gain ratio, information gain, support vector machine, and principal component analysis. The integrated framework for palm tree detection outperformed RF, SVM, and k-NN classification algorithms with an overall accuracy of 91.88% and 87.03%, date palm class-specific accuracies of 0.91 and 0.89, and kappa coefficients of 0.90 and 0.85 for the first and second study areas, respectively. The proposed integrated methodology demonstrated a highly efficient and promising tool to detect and map date palm trees from VHSR aerial images.

scholarly journals PALM TREE DETECTION USING CIRCULAR AUTOCORRELATION OF POLAR SHAPE MATRIX

2016 ◽  
Vol III-3 ◽  
pp. 465-472 ◽  
Author(s):  
A. Manandhar ◽  
L. Hoegner ◽  
U. Stilla
Keyword(s):  
Matrix Representation ◽  
Local Maximum ◽  
Detection Algorithm ◽  
Aerial Images ◽  
Support Vector ◽  
Palm Tree ◽  
Shape Feature ◽  
Tree Detection ◽  
Palm Trees ◽  
Increasing Demand

Palm trees play an important role as they are widely used in a variety of products including oil and bio-fuel. Increasing demand and growing cultivation have created a necessity in planned farming and the monitoring different aspects like inventory keeping, health, size etc. The large cultivation regions of palm trees motivate the use of remote sensing to produce such data. This study proposes an object detection methodology on the aerial images, using shape feature for detecting and counting palm trees, which can support an inventory. The study uses circular autocorrelation of the polar shape matrix representation of an image, as the shape feature, and the linear support vector machine to standardize and reduce dimensions of the feature. Finally, the study uses local maximum detection algorithm on the spatial distribution of standardized feature to detect palm trees. The method was applied to 8 images chosen from different tough scenarios and it performed on average with an accuracy of 84% and 76.1%, despite being subjected to different challenging conditions in the chosen test images.

scholarly journals PALM TREE DETECTION USING CIRCULAR AUTOCORRELATION OF POLAR SHAPE MATRIX

2016 ◽  
Vol III-3 ◽  
pp. 465-472 ◽  
Author(s):  
A. Manandhar ◽  
L. Hoegner ◽  
U. Stilla
Keyword(s):  
Matrix Representation ◽  
Local Maximum ◽  
Detection Algorithm ◽  
Aerial Images ◽  
Support Vector ◽  
Palm Tree ◽  
Shape Feature ◽  
Tree Detection ◽  
Palm Trees ◽  
Increasing Demand

Palm trees play an important role as they are widely used in a variety of products including oil and bio-fuel. Increasing demand and growing cultivation have created a necessity in planned farming and the monitoring different aspects like inventory keeping, health, size etc. The large cultivation regions of palm trees motivate the use of remote sensing to produce such data. This study proposes an object detection methodology on the aerial images, using shape feature for detecting and counting palm trees, which can support an inventory. The study uses circular autocorrelation of the polar shape matrix representation of an image, as the shape feature, and the linear support vector machine to standardize and reduce dimensions of the feature. Finally, the study uses local maximum detection algorithm on the spatial distribution of standardized feature to detect palm trees. The method was applied to 8 images chosen from different tough scenarios and it performed on average with an accuracy of 84% and 76.1%, despite being subjected to different challenging conditions in the chosen test images.

EFFECTS OF REPLACING CEMENT BY DATE PALM TREES WASTES ON CONCRETE PERFORMANCE

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ahmad Salah Edeen Nassef ◽  
Kalifa Hamed AlMuqbali ◽  
Sheikha Mahmood Al Naqabi
Keyword(s):  
Compressive Strength ◽  
Date Palm ◽  
Cement Content ◽  
Concrete Strength ◽  
Palm Tree ◽  
Compression Tests ◽  
Tree Leaves ◽  
Concrete Compressive Strength ◽  
Sustainable Environment ◽  
Palm Trees

This paper was studying the effects of palm tree wastes on the behavior of the concrete to reduce cement content in the concrete to ensure a sustainable environment. Both fibers of palm tree and the ash of palm tree leaves are used in this study considering different percentages of palm tree wastes, which are replaced the cement, to investigate both of workability and strength of the concrete. Also, the combination of palm tree leaves ash and fibers of palm trees is investigated. The slump and compression tests are carried out to evaluate both workability and concrete strength. The palm fibers were reducing the workability of concrete at both of different percentage of replacement and different fiber lengths. The slump is reduced by 26.667% at 2 cm fibers length and it is completely lost at 5 cm length fibers at the same percentage of replacement of 5% of the cement content. The palm fibers were weakening concrete compressive strength at different percentages and different fiber lengths. Palm leaves ash was enhancing concrete workability and concrete compressive strength.

Deep neural network based date palm tree detection in drone imagery

2022 ◽  
Vol 192 ◽  
pp. 106560
Author(s):  
Thani Jintasuttisak ◽  
Eran Edirisinghe ◽  
Ali Elbattay
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Date Palm ◽  
Palm Tree ◽  
Tree Detection

scholarly journals Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV Imagery

2019 ◽  
Vol 12 (1) ◽  
pp. 9 ◽  
Author(s):  
Ximena Tagle Casapia ◽  
Lourdes Falen ◽  
Harm Bartholomeus ◽  
Rodolfo Cárdenas ◽  
Gerardo Flores ◽  
...  
Keyword(s):  
Sustainable Management ◽  
Forest Products ◽  
Palm Tree ◽  
Kappa Index ◽  
Small Areas ◽  
Tree Crowns ◽  
Palm Trees ◽  
Object Based ◽  
Non Timber Forest Products ◽  
Palm Fruits

Sustainable management of non-timber forest products such as palm fruits is crucial for the long-term conservation of intact forest. A major limitation to expanding sustainable management of palms has been the need for precise information about the resources at scales of tens to hundreds of hectares, while typical ground-based surveys only sample small areas. In recent years, small unmanned aerial vehicles (UAVs) have become an important tool for mapping forest areas as they are cheap and easy to transport, and they provide high spatial resolution imagery of remote areas. We developed an object-based classification workflow for RGB UAV imagery which aims to identify and delineate palm tree crowns in the tropical rainforest by combining image processing and GIS functionalities using color and textural information in an integrative way to show one of the potential uses of UAVs in tropical forests. Ten permanent forest plots with 1170 reference palm trees were assessed from October to December 2017. The results indicate that palm tree crowns could be clearly identified and, in some cases, quantified following the workflow. The best results were obtained using the random forest classifier with an 85% overall accuracy and 0.82 kappa index.

scholarly journals Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel

2021 ◽  
Vol 13 (17) ◽  
pp. 3488
Author(s):  
Keren Goldberg ◽  
Ittai Herrmann ◽  
Uri Hochberg ◽  
Offer Rozenstein
Keyword(s):  
Time Series ◽  
Mean Shift ◽  
Vegetation Indices ◽  
Gradient Boosting ◽  
Support Vector ◽  
Object Based ◽  
Accuracy Measure ◽  
Extreme Gradient Boosting ◽  
Accuracy Measures ◽  
Sentinel 2

The overarching aim of this research was to develop a method for deriving crop maps from a time series of Sentinel-2 images between 2017 and 2018 to address global challenges in agriculture and food security. This study is the first step towards improving crop mapping based on phenological features retrieved from an object-based time series on a national scale. Five main crops in Israel were classified: wheat, barley, cotton, carrot, and chickpea. To optimize the object-based classification process, different characteristics and inputs of the mean shift segmentation algorithm were tested, including vegetation indices, three-band combinations, and high/low emphasis on the spatial and spectral characteristics. Four known vegetation indices (VIs)-based time series were tested. Additionally, we compared two widely used machine learning methods for crop classification, support vector machine (SVM) and random forest (RF), in addition to a newer classifier, extreme gradient boosting (XGBoost). Lastly, we examined two accuracy measures—overall accuracy (OA) and area under the curve (AUC)—in order to optimally estimate the accuracy in the case of imbalanced class representation. Mean shift best performed when emphasizing both the spectral and spatial characteristics while using the green, red, and near-infrared (NIR) bands as input. Both accuracy measures showed that RF and XGBoost classified different types of crops with significantly greater success than achieved by SVM. Nevertheless, AUC was better able to represent the significant differences between the classification algorithms than OA was. None of the VIs showed a significantly higher contribution to the classification. However, normalized difference infrared index (NDII) with XGBoost classifier showed the highest AUC results (88%). This study demonstrates that the short-wave infrared (SWIR) band with XGBoost improves crop type classification results. Furthermore, the study emphasizes the importance of addressing imbalanced classification datasets by using a proper accuracy measure. Since object-based classification and phenological features derived from a VI-based time series are widely used to produce crop maps, the current study is also relevant for operational agricultural management and informatics at large scales.

scholarly journals Oil Palm Tree Detection and Health Classification on High-Resolution Imagery Using Deep Learning

Agriculture ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 183
Author(s):  
Kanitta Yarak ◽  
Apichon Witayangkurn ◽  
Kunnaree Kritiyutanont ◽  
Chomchanok Arunplod ◽  
Ryosuke Shibasaki
Keyword(s):  
High Resolution ◽  
Oil Palm ◽  
Ground Truth ◽  
Palm Tree ◽  
Effective Management ◽  
Visual Interpretation ◽  
Healthy Tree ◽  
Tree Detection ◽  
Palm Trees ◽  
High Resolution Imagery

Combining modern technology and agriculture is an important consideration for the effective management of oil palm trees. In this study, an alternative method for oil palm tree management is proposed by applying high-resolution imagery, combined with Faster-RCNN, for automatic detection and health classification of oil palm trees. This study used a total of 4172 bounding boxes of healthy and unhealthy palm trees, constructed from 2000 pixel × 2000 pixel images. Of the total dataset, 90% was used for training and 10% was prepared for testing using Resnet-50 and VGG-16. Three techniques were used to assess the models’ performance: model training evaluation, evaluation using visual interpretation, and ground sampling inspections. The study identified three characteristics needed for detection and health classification: crown size, color, and density. The optimal altitude to capture images for detection and classification was determined to be 100 m, although the model showed satisfactory performance up to 140 m. For oil palm tree detection, healthy tree identification, and unhealthy tree identification, Resnet-50 obtained F1-scores of 95.09%, 92.07%, and 86.96%, respectively, with respect to visual interpretation ground truth and 97.67%, 95.30%, and 57.14%, respectively, with respect to ground sampling inspection ground truth. Resnet-50 yielded better F1-scores than VGG-16 in both evaluations. Therefore, the proposed method is well suited for the effective management of crops.

scholarly journals AN EVALUATION OF INVASIVE PEST, RED PALM WEEVIL RHYNCHOPHORUS FERRUGINEUS (OLIVIER, 1790) (COLEOPTERA, CURCULIONIDAE) POPULATION IN IRAQ

2020 ◽  
Vol 16 (2) ◽  
pp. 203-218
Author(s):  
Mohammed M. Alderawii ◽  
◽  
Aqeel A. Alyousuf ◽  
Samir A. Hasan ◽  
Jasim K. Mohammed ◽  
...  
Keyword(s):  
Date Palm ◽  
Monitoring Program ◽  
Invasive Pest ◽  
Palm Tree ◽  
Rhynchophorus Ferrugineus ◽  
Red Palm Weevil ◽  
Palm Trees ◽  
Palm Weevil ◽  
Baited Traps ◽  
Management Protocol

The Red Palm Weevil (RPW), Rhynchophorus ferrugineus (Olivier, 1790) is a devastating invasive pest of palm trees, invading the Iraqi date palm tree in 2015 for the first time in Safwan county, Basrah province. The Red Palm weevil has been categorized as a quarantine pest of date palm trees worldwide. In this study, a five years monitoring program has been achieved by scouting the invasive pest RPW population in Safwan county by using visual sampling and Pheromone baited traps. The results indicated that the number of infested palms, increased from 12 trees in 2015 to 111 in 16 orchards in 2016. The number of the infested palms was minimized to 3 trees in the county in 2019 due to the management protocol of the Ministry of Agriculture. Furthermore, the results of RPW adults appeared monthly in the county with two activity peaks during the moderate-temperature-months. In conclusion, the quarantine and management protocol of RPW decreased the population of the invasive pest which did not spread to other districts of Iraq.

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