scholarly journals Development of 360-degree imaging system for fresh fruit bunch (FFB) identification

2021 ◽  
Vol 1 (4) ◽  
pp. 1-9
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Imaging System ◽  
Fresh Fruit ◽  
Physical Contact ◽  
New Method ◽  
Coefficient Of Determination ◽  
Palm Tree ◽  
Fresh Fruit Bunch ◽  
Video Acquisition

In every cycle of harvesting operation, farmer does not have any information on how many bunches and which oil palm tree will be harvested. By introducing the 360ᵒ camera imaging system, number of Fresh Fruit Bunch (FFB) can be determined for every tree in a plantation area. Black bunch census was done manually to estimate yield. This was improved by video acquisition using a high resolution 360ᵒ camera integrated with an image processing software for video image processing to calculate number of FFB. Based on the standard planting pattern, it is time consuming process to circle each tree to acquire the 360ᵒ view of each tree. Current technology to approach bunches is destructive and conventional since the process involve physical contact between workers and FFB. Thus, a new method was established by the execution of All-Terrain Vehicle (ATV) between rows in plantation area for video acquisition. Images were extracted and threshold by using MATLAB software. L*, a*, and b* color space was used for the bunch identification throughout 90 samples of images to identify the mean intensity value. Model threshold verification for another 48 samples of images resulted with Coefficient of Determination, R2 of 0.8029 for bunch identification. As a result, a new method for video acquisition was established as well as processing method for bunch identification for large scale plantation area.

scholarly journals Conceptual Development of Automated Harvester for Tall Oil Palm Tree

2020 ◽  
Author(s):  
Mohd Hudzari ◽  
Muhammad Aliuddin Bakar ◽  
Muhammad Syukri Mohd Sabir
Keyword(s):  
Oil Palm ◽  
Conceptual Development ◽  
Industrial Revolution ◽  
Fresh Fruit ◽  
Palm Tree ◽  
Tall Oil ◽  
Fresh Fruit Bunch ◽  
Advantages And Disadvantages ◽  
Innovation And Invention ◽  
Fresh Fruit Bunches

Innovation and invention in field mechanization for oil palm sector has created a variety of advancement in technology. The change in oil palm operation to mechanization will overcome the problem of labour shortage occurrence in oil palm sector. The problem occurs in harvesting tall oil palm is the height of oil palm that causes difficulty to cut the fresh fruit bunch by using manual labour. Moreover, the use of automated harvester also will make the harvesting operation easier without the requirement of skilled labour and ensuring labour safety. The automated harvester has advantages and disadvantages that need to be improved in meeting the oil palm requirement. This study overviews the mechanization that are used in harvesting tall oil palm. This research project has resulted in the development of high technology mechanization based on previously developed machine for harvesting fresh fruit bunches (FFB) at 10 meter and above of oil palm age tress. However, the previous developed machines cannot be accepted in the current and widely practiced Industrial revolution 4.0 (IR4.0). Mechanization approach makes harvesting tall oil palm a reality to overcome the problem that normally occur in oil palm sector due to height of oil palm.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

New method of morphological color image processing

2010 ◽  
Vol 30 (8) ◽  
pp. 2101-2104
Author(s):  
Hong-zhong TANG ◽  
Hui-xian HUANG ◽  
Xue-feng GUO ◽  
Ye-wei XIAO
Keyword(s):  
Image Processing ◽  
Color Image ◽  
New Method ◽  
Color Image Processing

scholarly journals Identification of Efficient Sampling Techniques for Probabilistic Voltage Stability Analysis of Renewable-Rich Power Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2328
Author(s):  
Mohammed Alzubaidi ◽  
Kazi N. Hasan ◽  
Lasantha Meegahapola ◽  
Mir Toufikur Rahman
Keyword(s):  
Monte Carlo ◽  
Stability Analysis ◽  
Power Systems ◽  
Large Scale ◽  
Voltage Stability ◽  
Sampling Technique ◽  
Coefficient Of Determination ◽  
Sampling Techniques ◽  
Quasi Monte Carlo ◽  
Efficient Sampling

This paper presents a comparative analysis of six sampling techniques to identify an efficient and accurate sampling technique to be applied to probabilistic voltage stability assessment in large-scale power systems. In this study, six different sampling techniques are investigated and compared to each other in terms of their accuracy and efficiency, including Monte Carlo (MC), three versions of Quasi-Monte Carlo (QMC), i.e., Sobol, Halton, and Latin Hypercube, Markov Chain MC (MCMC), and importance sampling (IS) technique, to evaluate their suitability for application with probabilistic voltage stability analysis in large-scale uncertain power systems. The coefficient of determination (R2) and root mean square error (RMSE) are calculated to measure the accuracy and the efficiency of the sampling techniques compared to each other. All the six sampling techniques provide more than 99% accuracy by producing a large number of wind speed random samples (8760 samples). In terms of efficiency, on the other hand, the three versions of QMC are the most efficient sampling techniques, providing more than 96% accuracy with only a small number of generated samples (150 samples) compared to other techniques.

New Harvesting and Curing Techniques in the Production of Breeder's Seed Peanuts1

1979 ◽  
Vol 6 (2) ◽  
pp. 70-72
Author(s):  
T. A. Coffelt ◽  
F. S. Wright ◽  
J. L. Steele
Keyword(s):  
Large Scale ◽  
Combine Method ◽  
Frost Damage ◽  
New Method ◽  
Experimental Equipment ◽  
Harvesting Method ◽  
Curing Methods ◽  
Labor Requirements ◽  
High Level ◽  
Varietal Purity

Abstract A new method of harvesting and curing breeder's seed peanuts in Virginia was initiated that would 1) reduce the labor requirements, 2) maintain a high level of germination, 3) maintain varietal purity at 100%, and 4) reduce the risk of frost damage. Three possible harvesting and curing methods were studied. The traditional stack-pole method satisfied the latter 3 objectives, but not the first. The windrow-combine method satisfied the first 2 objectives, but not the last 2. The direct harvesting method satisfied all four objectives. The experimental equipment and curing procedures for direct harvesting had been developed but not tested on a large scale for seed harvesting. This method has been used in Virginia to produce breeder's seed of 3 peanut varieties (Florigiant, VA 72R and VA 61R) during five years. Compared to the stackpole method, labor requirements have been reduced, satisfactory levels of germination and varietal purity have been obtained, and the risk of frost damage has been minimized.

scholarly journals Rapid Single Image-Based DTM Estimation from ExoMars TGO CaSSIS Images Using Generative Adversarial U-Nets

2021 ◽  
Vol 13 (15) ◽  
pp. 2877
Author(s):  
Yu Tao ◽  
Siting Xiong ◽  
Susan J. Conway ◽  
Jan-Peter Muller ◽  
Anthony Guimpier ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Imaging System ◽  
Landing Site ◽  
Parameter Tuning ◽  
Processing Parameters ◽  
Input Image ◽  
Large Area ◽  
Laser Altimeter ◽  
Imaging Science

The lack of adequate stereo coverage and where available, lengthy processing time, various artefacts, and unsatisfactory quality and complexity of automating the selection of the best set of processing parameters, have long been big barriers for large-area planetary 3D mapping. In this paper, we propose a deep learning-based solution, called MADNet (Multi-scale generative Adversarial u-net with Dense convolutional and up-projection blocks), that avoids or resolves all of the above issues. We demonstrate the wide applicability of this technique with the ExoMars Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) 4.6 m/pixel images on Mars. Only a single input image and a coarse global 3D reference are required, without knowing any camera models or imaging parameters, to produce high-quality and high-resolution full-strip Digital Terrain Models (DTMs) in a few seconds. In this paper, we discuss technical details of the MADNet system and provide detailed comparisons and assessments of the results. The resultant MADNet 8 m/pixel CaSSIS DTMs are qualitatively very similar to the 1 m/pixel HiRISE DTMs. The resultant MADNet CaSSIS DTMs display excellent agreement with nested Mars Reconnaissance Orbiter Context Camera (CTX), Mars Express’s High-Resolution Stereo Camera (HRSC), and Mars Orbiter Laser Altimeter (MOLA) DTMs at large-scale, and meanwhile, show fairly good correlation with the High-Resolution Imaging Science Experiment (HiRISE) DTMs for fine-scale details. In addition, we show how MADNet outperforms traditional photogrammetric methods, both on speed and quality, for other datasets like HRSC, CTX, and HiRISE, without any parameter tuning or re-training of the model. We demonstrate the results for Oxia Planum (the landing site of the European Space Agency’s Rosalind Franklin ExoMars rover 2023) and a couple of sites of high scientific interest.

Distribution map of oil palm fresh fruit bunch using LiDAR*

2021 ◽  
Author(s):  
Husna Sarirah Husin ◽  
Nurnasuha Amar ◽  
Aznida Abu Bakar Sajak ◽  
Mohd Sallehin Mohd Kassim
Keyword(s):  
Oil Palm ◽  
Fresh Fruit ◽  
Distribution Map ◽  
Fresh Fruit Bunch

scholarly journals TreeMerge: a new method for improving the scalability of species tree estimation methods

2019 ◽  
Vol 35 (14) ◽  
pp. i417-i426 ◽  
Author(s):  
Erin K Molloy ◽  
Tandy Warnow
Keyword(s):  
Large Scale ◽  
Species Tree ◽  
New Method ◽  
Divide And Conquer ◽  
Supplementary Information ◽  
Estimation Methods ◽  
Running Time ◽  
Tree Estimation ◽  
Computationally Intensive ◽  
A Minor

Abstract Motivation At RECOMB-CG 2018, we presented NJMerge and showed that it could be used within a divide-and-conquer framework to scale computationally intensive methods for species tree estimation to larger datasets. However, NJMerge has two significant limitations: it can fail to return a tree and, when used within the proposed divide-and-conquer framework, has O(n5) running time for datasets with n species. Results Here we present a new method called ‘TreeMerge’ that improves on NJMerge in two ways: it is guaranteed to return a tree and it has dramatically faster running time within the same divide-and-conquer framework—only O(n2) time. We use a simulation study to evaluate TreeMerge in the context of multi-locus species tree estimation with two leading methods, ASTRAL-III and RAxML. We find that the divide-and-conquer framework using TreeMerge has a minor impact on species tree accuracy, dramatically reduces running time, and enables both ASTRAL-III and RAxML to complete on datasets (that they would otherwise fail on), when given 64 GB of memory and 48 h maximum running time. Thus, TreeMerge is a step toward a larger vision of enabling researchers with limited computational resources to perform large-scale species tree estimation, which we call Phylogenomics for All. Availability and implementation TreeMerge is publicly available on Github (http://github.com/ekmolloy/treemerge). Supplementary information Supplementary data are available at Bioinformatics online.

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