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Physio-Chemical And Functional Properties Of Nine Proso Millet Cultivars, Manjot Singh, Akinbode A. Adedeji, Dipak Santra
Physio-Chemical And Functional Properties Of Nine Proso Millet Cultivars, Manjot Singh, Akinbode A. Adedeji, Dipak Santra
Biosystems and Agricultural Engineering Faculty Publications
Evaluation of the postharvest properties of nine proso millet cultivars was carried out to determine their physical and engineering properties, which are very useful for designing appropriate systems for process operations such as sorting, drying, heating, cooling, and milling. Nine cultivars of proso millet comprising waxy and non-waxy types, namely Cope, Earlybird, Huntsman, Minco, Plateau, Sunrise, Rise, Dawn, and Panhandle, were obtained from the Panhandle Research and Extension Center, University of Nebraska, Scottsbluff. Results showed significant (p < 0.05) differences in their physical properties, such as sphericity, volume, bulk density, porosity, and angle of repose, which ranged from 0.86 to 0.91, from 3.94 to 5.14 mm3, from 765.49 to 809.67 kg m-3, from 42.49% to 44.20%, and from 22.98° to 25.74°, respectively. The cultivars were …
Stored Grain Pack Factor Measurements For Soybeans, Grain Sorghum, Oats, Barley, And Wheat, Rumela Bhadra, Mark E. Casada, Aaron P. Turner, Michael D. Montross, Sidney A. Thompson, Samuel G. Mcneill, Ronaldo G. Maghirang, Josephine M. Boac
Stored Grain Pack Factor Measurements For Soybeans, Grain Sorghum, Oats, Barley, And Wheat, Rumela Bhadra, Mark E. Casada, Aaron P. Turner, Michael D. Montross, Sidney A. Thompson, Samuel G. Mcneill, Ronaldo G. Maghirang, Josephine M. Boac
Biosystems and Agricultural Engineering Faculty Publications
Grain and oilseed crops stored in bins undergo compaction due to overbearing pressure of the grain inside the structure. Thus, volume measurements of grain in bins need to be combined with the amount of packing (usually called pack factor) in addition to the initial density so that the mass in the structure can be calculated. Multiple pack factor prediction methods are in use in the grain industry, but they have only been validated in the literature and compared with field data for corn and hard red winter wheat. Predictions from WPACKING, the program in ASABE Standard EP413.2, and two standard …
Application Of Acoustic Emission And Machine Learning To Detect Codling Moth Infested Apples, Mengxing Li, Nader Ekramirad, Ahmed Rady, Akinbode A. Adedeji
Application Of Acoustic Emission And Machine Learning To Detect Codling Moth Infested Apples, Mengxing Li, Nader Ekramirad, Ahmed Rady, Akinbode A. Adedeji
Biosystems and Agricultural Engineering Faculty Publications
Incidence of codling moth (CM) (Cydia pomonella L.) infestation in apples has been a major concern in North America for decades. CM larvae bore deep into the fruit, making it unmarketable. An effective noninvasive method to detect larvae-infested apples is necessary to ensure that apples are CM-free in post-harvest processing. In this study, a novel approach using an acoustic emission (AE) system and subsequent machine learning methods was applied to classify larvae-infested apples from intact apples. 'GoldRush‘ apples were infested with CM neonates and stored at the same conditions as intact apples. The AE system was used to collect …