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RISS 인기검색어
- 검색키워드
- 저자 : ( Clyde Fraisse ) (검색결과 4 건)
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Strawberry Plant Wetness Detection Using Color and Thermal Imaging
Swarup Anushka,이원석,Peres Natalia,Fraisse Clyde 한국농업기계학회 2020 바이오시스템공학 Vol.45 No.4
Purpose Leaf wetness is the presence of free water on the surface of a crop canopy. It is mainly caused due to factors such as rainfall, dew, and irrigation. The duration for which water is present on the crop surface is called the leaf wetness duration (LWD). The relationship between leaf wetness and plant disease has been studied for centuries. It has been found that the rate of infection is directly linked to the temperature of plants during the wet periods. Thus, it is imperative to detect and monitor the presence of water on the plant surface. Methods Currently, the most popular solution for this purpose is to use electronic leaf wetness sensors which are difficult to calibrate and are not user friendly. This research aimed at detecting leaf wetness in strawberry plants using noninvasive optical methods such as color and thermal imaging. Results It was found that color imaging yielded good classification results for differentiating between wet and dry leaf surfaces. However, its dependence on illumination conditions was a drawback. Thermal imaging proved to be useful when using highresolution cameras for water droplet detection but was dependent on ambient weather conditions. Conclusion The results suggested that fusing color and thermal imaging technologies could compensate for the drawbacks present when using these technologies individually and could prove to be potent in detecting leaf wetness.
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Automated Strawberry Flower Detection for Yield Estimation using Machine Vision
( Arumugam Kalaikannan ),( Won Suk Lee ),( Natalia Peres ),( Clyde Fraisse ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1
Strawberry is an important horticultural crop in the U.S. Strawberry, which ranks 8th in produce and 4th in fruit with a total value of $2.4 billion annually. Since strawberries are manually harvested and labor shortage is a major concern, yield predictions become extremely important for scheduling labors in harvesting and other field operations as well as marketing. For efficient use of labor resources in harvesting and other operations, strawberry growers in the U.S. will need reliable yield prediction models by utilizing a more feasible and efficient method to count the number of flowers in their fields. Images were acquired from a strawberry field using a consumer grade digital color camera at various working distances, angles and lighting conditions. Various image processing techniques were used to develop automated flower counting algorithms from the images. An accuracy of 88% was achieved by the computer vision algorithm on validation dataset. The number of strawberry flowers obtained from this algorithm will be used to develop a strawberry yield prediction model, which eventually will be used for efficient labor management for harvesting and marketing to increase yield and profit of the strawberry growers.
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Comparison of Remote Sensing and Crop Growth Models for Estimating Within-Field LAI Variability
Hong, Suk-Young,Sudduth, Kenneth-A.,Kitchen, Newell-R.,Fraisse, Clyde-W.,Palm, Harlan-L.,Wiebold, William-J. The Korean Society of Remote Sensing 2004 大韓遠隔探査學會誌 Vol.20 No.3
The objectives of this study were to estimate leaf area index (LAI) as a function of image-derived vegetation indices, and to compare measured and estimated LAI to the results of crop model simulation. Soil moisture, crop phenology, and LAI data were obtained several times during the 2001 growing season at monitoring sites established in two central Missouri experimental fields, one planted to com (Zea mays L.) and the other planted to soybean (Glycine max L.). Hyper- and multi-spectral images at varying spatial. and spectral resolutions were acquired from both airborne and satellite platforms, and data were extracted to calculate standard vegetative indices (normalized difference vegetative index, NDVI; ratio vegetative index, RVI; and soil-adjusted vegetative index, SAVI). When comparing these three indices, regressions for measured LAI were of similar quality $(r^2$ =0.59 to 0.61 for com; $r^2$ =0.66 to 0.68 for soybean) in this single-year dataset. CERES(Crop Environment Resource Synthesis)-Maize and CROPGRO-Soybean models were calibrated to measured soil moisture and yield data and used to simulate LAI over the growing season. The CERES-Maize model over-predicted LAI at all corn monitoring sites. Simulated LAI from CROPGRO-Soybean was similar to observed and image-estimated LA! for most soybean monitoring sites. These results suggest crop growth model predictions might be improved by incorporating image-estimated LAI. Greater improvements might be expected with com than with soybean.
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Comparison of Remote Sensing and Crop Growth Models for Estimating Within-Field LAI Variability
Suk Young Hong,Kenneth A. Sudduth,Newell R. Kitchen,Clyde W. Fraisse,Harlan L. Palm,William J. Wiebold 大韓遠隔探査學會 2004 大韓遠隔探査學會誌 Vol.20 No.3
The objectives of this study were to estimate leaf area index (LAI) as a function of image-derived vegetation indices, and to compare measured and estimated LAI to the results of crop model simulation. Soil moisture, crop phenology, and LAI data were obtained several times during the 2001 growing season at monitoring sites established in two central Missouri experimental fields, one planted to com (Zea mays L.) and the other planted to soybean (Glycine max L.). Hyper- and multi-spectral images at varying spatial and spectral resolutions were acquired from both airborne and satellite platforms, and data were extracted to calculate standard vegetative indices (normalized difference vegetative index, NDVI; ratio vegetative index, RVI; and soil-adjusted vegetative index, SAVI). When comparing these three indices, regressions for measured LAI were of similar quality (r2=0.59 to 0.61 for com; r2=0.66 to 0.68 for soybean) in this single-year dataset. CERES(Crop Environment REsource Synthesis)-Maize and CROPGRO-Soybean models were calibrated to measured soil moisture and yield data and used to simulate LAI over the growing season. The CERES-Maize model over-predicted LAI at all corn monitoring sites. Simulated LAI from CROPGRO-Soybean was similar to observed and image-estimated LAI for most soybean monitoring sites. These results suggest crop growth model predictions might be improved by incorporating image-estimated LAI. Greater improvements might be expected with com than with soybean.
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