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 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 STUDI PEREMAJAAN TANAMAN KELAPA SAWIT DI AREAL LAHAN TANAH MINERAL DAN LAHAN GAMBUT

2020 ◽  
Vol 9 (3) ◽  
pp. 201
Author(s):  
Gatot Pramuhadi ◽  
Muhammad Aji Setiawan ◽  
Nenda Fuji Putri Daliesta
Keyword(s):  
Oil Palm ◽  
Peat Soil ◽  
Mineral Soil ◽  
Field Capacity ◽  
Large Field ◽  
Total Field ◽  
Palm Fruit ◽  
Palm Trees ◽  
Mineral Soils ◽  
North Sumatra

Oil palm trees that are more than 25 years old generally have low productivity of oil palm fruit and will decrease in productivity. For this reason, it is necessary to replant oil palm plants so that their productivity can be maintained high and increased. The aim of this research is to study the technique of replanting oil palm plants in mineral soil areas in the area of Kebun Aek Loba, PT Socfindo, Asahan Regency, North Sumatra Province and in the peatland area of Kebun Mandah Estate, PT Bhumireksa Nusa Sejati, Riau Province. Soil types in the Aek Loba garden area were dominated by mineral soils of the typic hapludult class and made from mother rock tufo toba and young alluvium with a soil pH of 5.5. Mandah Estate plantation area is 100% peat soil with a composition of 74% mature peat (sapric) and 26% medium peat (hemic). Peat is more than 3 meters deep (including very deep peat) and has a crumbly texture so it is easy to erode. The peat soil includes ombrogin peat (formed from large swampy plains), has a high acid content with a pH < 5.5, and has a peat soil density of (1.25 - 1.80) g / cc. Replanting activities in mineral soil areas include ripping, plowing, chipping, holing to planting with a large field capacity (0.04 - 0.72) ha / hour and a total time of completion of work (total field time) of 139.5 hours / ha. Replanting activities in peatland areas include falling, chipping, deboling, holing and planting with a large field capacity (0.06 - 1.25) ha / hour and a total field time of 73.7 hours / ha. Keywords: field capacity, mineral soils, peatlands, replanting, total field time

scholarly journals Identifikasi Cendawan Mikoriza arbuskular (CMA) pada Beberapa Tekstur

Buletin Palma ◽  
2016 ◽  
Vol 16 (2) ◽  
pp. 203
Author(s):  
ENGELBERT MANAROINSONG ◽  
A. A. LOLONG
Keyword(s):  
Soil Texture ◽  
Oil Palm ◽  
Soil Microorganisms ◽  
Peat Soil ◽  
Arbuscular Mycorrhizal ◽  
Soil Samples ◽  
Font Size ◽  
Central Kalimantan ◽  
Purposive Sampling ◽  
Palm Trees

<p><span style="font-size: medium;">ABSTRAK </span></p><p>Tanaman kelapa sawit ditanam hampir pada semua jenis tanah seperti gambut, liat berlempung dan lempung berpasir dengan proporsi kandungan tiap tekstur tanah berbeda-beda dilapangan. Cendawan mikoriza arbuskular (CMA) merupakan mikroorganisme tanah yang berperan sebagai mikroba perombak, membantu tanaman dalam penyerapan unsur hara dari tanah. Penelitian ini bertujuan mengidentifikasi jenis dan populasi spora mikoriza pada beberapa tekstur tanah dipertanaman kelapa sawit. Penelitian dilakukan pada 3 (tiga) lokasi perkebunan kelapa sawit di Kabupaten Sampit, Kalimantan Tengah, yaitu di lahan PT. Sapta Karya Damai (SKD), PT. Agro Bukit dan areal pertanaman kelapa dan kelapa sawit milik petani di Samuda besar. Penelitian dilaksanakan pada bulan Maret sampai dengan Juni 2015. Pengambilan contoh tanah dilakukan secara Purposive Sampling. Analisis contoh tanah dilakukan di  Laboratorium Pengujian Balittro - Bogor. Parameter yang diamati, yaitu pH, kelembaban tanah, jenis dan jumlah populasi spora mikoriza. Hasil penelitian menunjukan bahwa mikoriza yang ditemukan adalah dari jenis Glomus dengan populasi bervariasi menurut jenis tanah, yaitu berkisar antara 175,3 - 283 buah per 100 mg tanah. Populasi tertinggi, yaitu 283 spora per 100 mg tanah  ditemukan pada jenis tanah gambut di perkebunan kelapa sawit PT. Agro Bukit yang belum berproduksi (umur &lt;5 tahun). Populasi terendah, yaitu 175,3 spora per 100 mg tanah ditemukan pada tektur tanah liat berlempung di lokasi perkebunan kelapa sawit PT. Agro Bukit yang telah berproduksi normal (umur <br /> &gt;10 tahun).</p><p>Kata kunci : Cendawan, mikoriza, tekstur, tanah, kelapa sawit.</p><p> </p><p><strong><span style="font-size: medium;">Identification of The Fungie Micorhyza Asbuscular on Saveral Soil Textures <br />at Oilpalm Plantation, Central Kalimantan </span></strong></p><p><span style="font-size: medium;">ABSTRACT </span></p><p>Oil palm trees planted in virtually all types of soil such as peat, clay and sandy clay with argillaceous proportion of the content of each soil texture is different in the field. Arbuscular mycorrhizal fungie (FAM) is a soil microorganisms that act as microbial crusher, helps plants to absorb nutrients from the soil. This study aims to identify the types and populations of mycorrhizal spores in soil texture on oilpalm plantation. The study was conducted at three (3) locations oil palm plantations in the district of Sampit, Central Kalimantan, namely in te area of PT. Sapta Karya Damai (SKD), PT. Agro Bukit and coconut plantations and oil palm farmers in Samuda Besar. The experiment was conducted in March and June 2015. Soil sampling is purposive sampling. Analysis of soil samples carried out in the Laboratory Testing Balittro - Bogor. The parameters observed were pH, soil moisture, type and number of mycorrhizal spores population. The results showed that mycorrhizae are found are of the type Glomus with a population varies according to the type of soil ranged between 175.3 to 283 pieces per 100 mg of soil. The highest population, namely 283 spores per 100 mg of soil found in the peat soil types in PT. Agro Bukit oil palm plantations wihich is not yet in production (&lt;5 years). Lowest population is 175.3 per 100 mg of spores found in soil texture argillaceous clay in PT. Agro Bukit oil palm plantations who have production (&gt;10 years).</p>Keywords : Fungi, michorrizal,textur, soil, oil palm.

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.

PROPERTIES IMPROVEMENT AND HYDROPHOBICITY PREVENTION OF PEAT SOIL IN OIL PALM PLANTATION THROUGH STEEL SLAG APPLICATION

2016 ◽  
Vol 24 (1) ◽  
pp. 39-46
Author(s):  
Winarna Winarna ◽  
Iput Pradiko ◽  
Muhdan Syarovy ◽  
Fandi Hidayat
Keyword(s):  
Soil Ph ◽  
Oil Palm ◽  
Water Retention ◽  
Peat Soil ◽  
Steel Slag ◽  
Field Capacity ◽  
Ash Content ◽  
Oil Palm Plantation ◽  
Randomized Design ◽  
Four Levels

Development of oil palm plantation on peatland was faced with hydrophobicity problem caused by over drained. Hydrophobicity could reduce water retention and nutrient availability in the peat soil. Beside of proper water management application, addition of soil ameliorant which contain iron could increase stability and improve peat soil fertility. The study was conducted to obtain the effect of steel slag on peat soil properties and hydrophobicity. In this study, peat soil was incorporated with steel slag and incubated in 60 days period. The research was employed completely randomized design (CRD) factorial 2 x 2 x 4. First factor is peat maturity consists of two levels: sapric (S) and hemic (H), while the second factor is soil moisture which also consist of two levels: field capacity (W1) and dry (under the critical water content) (W2). The third factor is steel slag dosage which consist of four levels: 0 g pot (TB0), 7.17 g pot (TB1), 14.81 g -1 -1 pot (TB2), and 22.44 g pot (TB3). The result showed that application of steel slag significantly increase of soil pH, ash content, and water retention at pF 4.2. Furthermore, application of steel slag significantly reduce time for water reabsorption (wettability) in sapric. On the other hand, there are negative corellation between water penetration and soil pH, ash content, and water retention at pF 4.2. Overall, application of steel slag could increase wettability and prevent peat soil hydrophobicity.

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.

Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images

2021 ◽  
Vol 173 ◽  
pp. 95-121
Author(s):  
Juepeng Zheng ◽  
Haohuan Fu ◽  
Weijia Li ◽  
Wenzhao Wu ◽  
Le Yu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Oil Palm ◽  
Palm Trees ◽  
Aerial Vehicle ◽  
Uav Images

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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