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RISS 인기검색어
- 검색키워드
- 저자 : Shagor Sarkar (검색결과 2 건)
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( Shagor Sarkar ),( Sang-yoon Lee ),( Young-ki Park ),( Jae-kyung Yang ),( Gi-young Kweon ) 한국농업기계학회 2019 한국농업기계학회 학술발표논문집 Vol.24 No.1
Near infrared (NIR) predictions of sugar content have been made on kiwifruit (Actinidia arguta) using PLS regression method on high and low precision spectrophotometers spectra. A total of 1172 fruits of four species, namely Autumn sense (A), Chungsan (C), Daesung (D), and Green ball (Gb) were collected from five regions Gwangyang (G), Muju (M), Suwon (S), Wonju (W), and Yeongwol (Y). Sugar content prediction models were developed using region, species and combined (region, plus species) data. Firstly, High precision spectrophotometer of 730-2300 nm wavelengths was used to predict the sugar content and the PLS analysis of measured data showed excellent performance with R2 value of 0.75 or higher except for Yeongwol region, C, and Gb species. Next analysis was performed at a wavelength range of 729-1047 nm to compare the experiments performance of both spectrophotometers. The analysis of the comparison demonstrates the high precision spectrophotometer results were slightly superior to the low precision spectrophotometer and showed similar trends in all the cases of results. Finally, the β-coefficient was analyzed to select wavelengths that have the greatest influence on sugar content estimation where six wavelengths were selected, and sugar content was predicted. Using those six selected wavelengths, analysis showed unsatisfactory results. Future studies aimed to investigate influential variables using meteorological data and soil data to develop more reliable predictive models and optical sensing systems.
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Sangyoon Lee,Shagor Sarkar,Youngki Park,Jaekyeong Yang,Giyoung Kweon 경상대학교 농업생명과학연구원 2019 농업생명과학연구 Vol.53 No.3
In this study, we tried to find out the most appropriate pre-processing method and to verify the feasibility of developing a low-price sensing system for predicting the hardy kiwis sugar content based on VNIRS and subsequent spectral analysis. A total of 495 hardy kiwi samples were collected from three farms in Muju, Jeollabukdo, South Korea. The samples were scanned with a spectrophotometer in the range of 730-2300 nm with 1 nm spectral sampling interval. The measured data were arbitrarily separated into calibration and validation data for sugar content prediction. Partial least squares (PLS) regression was performed using various combinations of pre-processing methods. When the latent variable (LV) was 8 with the pre-processing combination of standard normal variate (SNV) and orthogonal signal correction (OSC), the highest R2 values of calibration and validation were 0.78 and 0.84, respectively. The possibility of predicting the sugar content of hardy kiwi was also examined at spectral sampling intervals of 6 and 10 nm in the narrower spectral range from 730 nm to 1200 nm for a low-price optical sensing system. The prediction performance had promising results with R2 values of 0.84 and 0.80 for 6 and 10 nm, respectively. Future studies will aim to develop a low-price optical sensing system with a combination of optical components such as photodiodes, light-emitting diodes (LEDs) and/or lamps, and to locate a more reliable prediction model by including meteorological data, soil data, and different varieties of hardy kiwi plants.
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