scholarly journals ANALISA CITRA HIPERSPEKTRAL DAUN DARI TANAMAN KELAPA SAWIT YANG MENGALAMI KEKURANGAN AIR MENGGUNAKAN PROGRAM MATLAB

2019 ◽  
Vol 16 (2) ◽  
pp. 143
Author(s):  
JR Lessy Eka Putri ◽  
Minarni Minarni ◽  
Feri Candra ◽  
Herman Herman
Keyword(s):  
Image Processing ◽  
Hyperspectral Imaging ◽  
Oil Palm ◽  
Imaging System ◽  
Water Volume ◽  
Imaging Method ◽  
Pixel Size ◽  
Water Deficiency ◽  
Image Recording ◽  
Detached Leaves

The hyperspectral imaging method has been widely and intensively used in agriculture to find out various problems that occur in plants. Image processing is very important step in an imaging method. This research aims to create Matlab based program to be used to analyze the leaf image of oil palm plants that has experienced water deficiency. Reflectance intensity values were extracted from the process. The hyperspectral imaging system consisted of a 650 nm diode laser, a spectrograph, monochrome CMOS camera, and Matlab image processing program. The samplesused were 8 month old Tenera variety of oil palm seedlings which were treated to simulate water deficiency in the form of variations in the volume of water, namely 0 mL (without watering), 1000 mL, 2000 mL, and 3000 mL (normal), 3 duplicates for each volume. The samples were given water volume of 1000 mL and 2000 mL for every 7 days in 21 days, while the sampleswith 3000 mL of water were watered every day. Image recording was done on the 21st day for detached leaves at the the bottom part.The results showed that the Matlab program was able to separate each row from 15 images, each of which had a pixel size of 1280 × 1024 and merge each of the same lines into 1024 images with a pixel size of 1280 × 15. The reflectance intensity values were then obtained. The results showed that higher levels of water deficiency in plants produce increasing reflectance intensity values.

scholarly journals Pencitraan Hiperspekral untuk Membedakan Asal Tanah Tumbuh Dari Tandan Buah Segar Kelapa Sawit

2020 ◽  
Vol 4 (3) ◽  
pp. 761
Author(s):  
Dina Veranita ◽  
Minarni Minarni ◽  
Feri Candra ◽  
Saktioto Saktioto ◽  
Mohammad Fisal Rabin
Keyword(s):  
Hyperspectral Imaging ◽  
Oil Palm ◽  
Fruits And Vegetables ◽  
Imaging System ◽  
Peat Soil ◽  
Mineral Soil ◽  
Plant Management ◽  
Imaging Method ◽  
Fruit Firmness ◽  
Palm Trees

Hyperspectral imaging is a non destructive method that has been used to evaluate internal characteristics of fruits and vegetables. Plant genetics, soil characteristics, and plant management are some of key factors to define the quality of oil palm fresh fruit bunches (FFB) produced. This research was aimed to discriminate the Tenera oil palm FFBs produced by oil palm trees grown from mineral soil and peat soil using a hyperspectral imaging system which utilized a Specim V10 spektrograf. The discrimination was based on their ripeness level, mesocarp firmness, and classification using K-mean clustering. The samples consisted of 61 mineral soil FFBs and 60 peat soil FFBs with three ripeness levels as unripe, ripe, and overripe. Hyperspectral images were recorded and processed using Matlab programs. The spectral reflectance intensities showed the discrimination between both origin soils at wavelength ranges of 700 nm  900 nm. The results also showed higher reflectance intensities of peat soil FFBs than mineral soil FFBs. Correspondingly, Fruit firmness of peat soil FFBs are higher than mineral soil FFBs. Classification using K- mean clustering between reflectance intensities and fruit firmness showed significant clusters for three ripeness levels. These results will be useful for an oil palm FFB sorting machine based on spectral imaging method

scholarly journals APLIKASI PROGRAM MATLAB UNTUK ANALISA CITRA HYPERSPECTRAL PADA AKAR DARI TANAMAN KELAPA SAWIT YANG MENGALAMI KEKURANGAN AIR

2019 ◽  
Vol 16 (2) ◽  
pp. 149
Author(s):  
Mailestari Wina Yance ◽  
Minarni Minarni ◽  
Feri Candra ◽  
Herman Herman
Keyword(s):  
Oil Palm ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Three Dimensional ◽  
Hyperspectral Images ◽  
Imaging Method ◽  
Water Deficiency ◽  
Hyperspectral Image Processing ◽  
Matlab Program ◽  
Water Volumes

Hyperspectral images are three dimensional images which have two dimension spatial information and one  dimension spectral information. Hyperspectral image processing using Matlab program is preferable because it is more adaptive for many analysis purposes. This research was aimed  to construct Matlab to process and analyze the hyperspectral images of the roots of oil palm plants that have experienced water deficiency. The program was designed and constructed using a GUI . The use of a GUI aims to combine each pixel of the same line from each sample to produce a new image. The samples were roots  of oil palm plants that experienced simulated water deficiency by giving different water volumes of 0 mL, 1000 mL, 2000 mL and 3000 mL (normal). The optical method used in this study is a hyperspectral imaging method which has 650 nm diode laser  as the light source , spectrograph Specim Imspector V10 , and a  monochrome CMOS as a detector. Reflectance intensity versus wavelength  was extracted from each images and analyzed. The results showed that the Matlab GUI program that had been constructed was able to produce 1024 new images that had a pixel size of 15× 1280 from each sample. The results also show that the reflectance intensity values are higher at higher water deficiency of the oil palm roots.

scholarly journals Hyperspectral characterisation of natural illumination in woodland and forest environments

2021 ◽  
Author(s):  
Li Shiwen ◽  
Laura Steel ◽  
Cecilia A. L. Dahlsjö ◽  
Stuart N. Peirson ◽  
Alexander Shenkin ◽  
...  
Keyword(s):  
Image Processing ◽  
Dynamic Range ◽  
Imaging System ◽  
High Dynamic Range ◽  
Light Environment ◽  
Imaging Method ◽  
Specular Reflectance ◽  
Processing Pipeline ◽  
Spatially Resolved ◽  
Spectrally Resolved

Light in nature is complex and dynamic, and varies along spectrum, space, direction, and time. While both spectrally resolved measurements and spatially resolved measurements are widely available, spectrally and spatially resolved measurements are technologically more challenging. Here, we present a portable imaging system using off-the-shelf components to capture the full spherical light environment in a spectrally and spatially resolved fashion. The method relies on imaging the 4π-steradian light field reflected from a mirrored chrome sphere using a commercial hyperspectral camera (400-1000 nm) from multiple directions and an image-processing pipeline for extraction of the mirror sphere, removal of saturated pixels, correction of specular reflectance of the sphere, promotion to a high dynamic range, correction of misalignment of images, correction of intensity compression, erasure of the imaging system, unwrapping of the spherical images, filling-in blank regions, and stitching images collected from different angles. We applied our method to Wytham Woods, an ancient semi-natural woodland near Oxford, UK. We acquired a total of 168 images in two sites with low and high abundance of ash, leading to differences in canopy, leading to a total 14 hyperspectral light probes. Our image-processing pipeline corrected small (<3 deg) field-based misalignment adequately. Our novel hyperspectral imaging method is adapted for field conditions and opens up novel opportunities for capturing the complex and dynamic nature of the light environment.

scholarly journals Hyperspectral Imaging Bioinspired by Chromatic Blur Vision in Color Blind Animals

Photonics ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 91
Author(s):  
Shuyue Zhan ◽  
Weiwen Zhou ◽  
Xu Ma ◽  
Hui Huang
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging System ◽  
Light Flux ◽  
Image Sensor ◽  
High Light ◽  
Detection Sensitivity ◽  
Imaging Method ◽  
Inverse Filtering ◽  
Color Blind ◽  
Blurred Images

Hyperspectral imaging remote sensing is mutually restricted in terms of spatial and spectral resolutions, signal-to-noise ratio and exposure time. To deal with this trade-off properly, it is beneficial for imaging systems to have high light flux. In this paper, we put forward a novel hyperspectral imaging method with high light flux bioinspired by chromatic blur vision in color blind animals. We designed a camera lens with high degree of longitudinal chromatic aberration, a monochrome image sensor captured the chromatic blur images at different focal lengths. Finally, by using the known point spread functions of the chromatic blur imaging system, we process these chromatically blurred images by deconvolution based on singular value decomposition inverse filtering, and the spectral images of a target were restored. We constructed three different targets for validating image restoration based on a typical octopus eyeball imaging system. The results show that the proposed imaging method can effectively extract spectral images from the chromatically blurred images. This study can facilitate development of a novel bionic hyperspectral imaging, which may benefit from the high light flux of a large aperture and provide higher detection sensitivity.

Mapping of fatty acids composition in shelled almonds analysed in bulk using a Hyperspectral Imaging system

LWT ◽  
2021 ◽  
Vol 138 ◽  
pp. 110678
Author(s):  
Irina Torres ◽  
Dolores Pérez-Marín ◽  
Miguel Vega-Castellote ◽  
María-Teresa Sánchez
Keyword(s):  
Fatty Acids ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Fatty Acids Composition

scholarly journals Optical Aberration Calibration and Correction of Photographic System Based on Wavefront Coding

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4011
Author(s):  
Chuanwei Yao ◽  
Yibing Shen
Keyword(s):  
Imaging System ◽  
Optical Design ◽  
Computational Imaging ◽  
Large Field ◽  
Wavefront Aberration ◽  
Imaging Method ◽  
Image Deconvolution ◽  
Pupil Function ◽  
Wavefront Coding ◽  
Blurred Images

The image deconvolution technique can recover potential sharp images from blurred images affected by aberrations. Obtaining the point spread function (PSF) of the imaging system accurately is a prerequisite for robust deconvolution. In this paper, a computational imaging method based on wavefront coding is proposed to reconstruct the wavefront aberration of a photographic system. Firstly, a group of images affected by local aberration is obtained by applying wavefront coding on the optical system’s spectral plane. Then, the PSF is recovered accurately by pupil function synthesis, and finally, the aberration-affected images are recovered by image deconvolution. After aberration correction, the image’s coefficient of variation and mean relative deviation are improved by 60% and 30%, respectively, and the image can reach the limit of resolution of the sensor, as proved by the resolution test board. Meanwhile, the method’s robust anti-noise capability is confirmed through simulation experiments. Through the conversion of the complexity of optical design to a post-processing algorithm, this method offers an economical and efficient strategy for obtaining high-resolution and high-quality images using a simple large-field lens.

scholarly journals Synthetic polarization-sensitive optical coherence tomography by deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yi Sun ◽  
Jianfeng Wang ◽  
Jindou Shi ◽  
Stephen A. Boppart
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Imaging System ◽  
Imaging Modality ◽  
Similarity Index ◽  
Polarization Sensitivity ◽  
Imaging Method ◽  
Optical Coherence ◽  
The Real ◽  
Oct Imaging

AbstractPolarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.

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