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.

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.

The Continuity of Palm Sugar Production: A Literature Review

2021 ◽  
Vol 6 (2) ◽  
pp. 37
Author(s):  
Liska Simamora ◽  
Damara Dinda Nirmalasari Zebua ◽  
Yoga Aji Handoko ◽  
Nugraheni Widyawati
Keyword(s):  
Developing Countries ◽  
Literature Review ◽  
Developed Countries ◽  
Palm Tree ◽  
Sugar Production ◽  
Food Ingredients ◽  
Palm Trees ◽  
Important Benefit ◽  
Palm Sugar ◽  
Increasing Demand

The demand for palm sugar is increasing since people prefer natural ingredients. Natural and organic food ingredients have the reputation of being healthier and safer than synthetic ingredients. This study will discuss one particular ingredient namely palm sugar which is known as a natural sweetener. Developed countries have started to import palm sugar as a natural sweetener from developing countries. The production of palm sugar in developing countries has an important benefit in the craftsman household economy. However, current production of palm sugar does not meet the increasing demand, which is caused mainly by the lack of farmers, palm sugar craftsmen’s inadequate knowledge of palm sugar production, and poor incorporation of technology both in the cultivation of palm tree and the production process of palm sugar. This literature review study aims to understand the continuity of palm sugar production within the following framework which consists of four steps: (1) identifying current situation, (2) identifying the problems, (3) presenting the resolution, (4) proposing programs and strategies. Based on this framework a few programs and strategies are generated to maintain the continuity of palm sugar production, they are: (1) Palm trees domestication, (2) research and development center for Indonesian palm sugar, and (3) ensuring a good collaboration among the stake holders involved.

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.

Oil Palm Tree Detection and Counting in Aerial Images Based on Faster R-CNN

2020 ◽  
pp. 475-482
Author(s):  
Xinni Liu ◽  
Kamarul Hawari Ghazali ◽  
Fengrong Han ◽  
Izzeldin Ibrahim Mohamed ◽  
Yue Zhao ◽  
...  
Keyword(s):  
Oil Palm ◽  
Aerial Images ◽  
Palm Tree ◽  
Tree Detection ◽  
Detection And Counting

scholarly journals Deep-Learning-Based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1458
Author(s):  
Adel Ammar ◽  
Anis Koubaa ◽  
Bilel Benjdira
Keyword(s):  
Deep Learning ◽  
Geographical Location ◽  
Network Models ◽  
Aerial Images ◽  
Palm Tree ◽  
Average Precision ◽  
Neural Network Models ◽  
Learning Framework ◽  
Palm Trees ◽  
Uav Images

In this paper, we propose an original deep learning framework for the automated counting and geolocation of palm trees from aerial images using convolutional neural networks. For this purpose, we collected aerial images from two different regions in Saudi Arabia, using two DJI drones, and we built a dataset of around 11,000 instances of palm trees. Then, we applied several recent convolutional neural network models (Faster R-CNN, YOLOv3, YOLOv4, and EfficientDet) to detect palms and other trees, and we conducted a complete comparative evaluation in terms of average precision and inference speed. YOLOv4 and EfficientDet-D5 yielded the best trade-off between accuracy and speed (up to 99% mean average precision and 7.4 FPS). Furthermore, using the geotagged metadata of aerial images, we used photogrammetry concepts and distance corrections to automatically detect the geographical location of detected palm trees. This geolocation technique was tested on two different types of drones (DJI Mavic Pro and Phantom 4 pro) and was assessed to provide an average geolocation accuracy that attains 1.6 m. This GPS tagging allows us to uniquely identify palm trees and count their number from a series of drone images, while correctly dealing with the issue of image overlapping. Moreover, this innovative combination between deep learning object detection and geolocalization can be generalized to any other objects in UAV images.

Image fire detection algorithm based on support vector machine

2010 ◽  
Vol 30 (4) ◽  
pp. 1129-1131
Author(s):  
Na-juan YANG ◽  
Hui-qin WANG ◽  
Zong-fang MA
Keyword(s):  
Support Vector Machine ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Support Vector

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

scholarly journals An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images

2020 ◽  
Vol 13 (1) ◽  
pp. 65
Author(s):  
Jingtao Li ◽  
Yonglin Shen ◽  
Chao Yang
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Short Term Memory ◽  
Complete Classification ◽  
Support Vector ◽  
Classification Models ◽  
Training Samples ◽  
Agricultural Applications ◽  
Crop Classification ◽  
Increasing Demand

Due to the increasing demand for the monitoring of crop conditions and food production, it is a challenging and meaningful task to identify crops from remote sensing images. The state-of the-art crop classification models are mostly built on supervised classification models such as support vector machines (SVM), convolutional neural networks (CNN), and long- and short-term memory neural networks (LSTM). Meanwhile, as an unsupervised generative model, the adversarial generative network (GAN) is rarely used to complete classification tasks for agricultural applications. In this work, we propose a new method that combines GAN, CNN, and LSTM models to classify crops of corn and soybeans from remote sensing time-series images, in which GAN’s discriminator was used as the final classifier. The method is feasible on the condition that the training samples are small, and it fully takes advantage of spectral, spatial, and phenology features of crops from satellite data. The classification experiments were conducted on crops of corn, soybeans, and others. To verify the effectiveness of the proposed method, comparisons with models of SVM, SegNet, CNN, LSTM, and different combinations were also conducted. The results show that our method achieved the best classification results, with the Kappa coefficient of 0.7933 and overall accuracy of 0.86. Experiments in other study areas also demonstrate the extensibility of the proposed method.

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