Determination of moisture content and contaminated blank dried figs (Ficus carica L.) using dielectric property and artificial neural network

Abstract Dried figs are a garden produce that must be graded after harvesting. Moisture levels and contaminated blank are two of the most critical effective elements on the marketability of dried figs, and they are highly related to fig quality. In the present research, an intelligent system was employed to classify dried figs based on moisture content levels and infected blank fruits. Capacitance characteristics, average diameter, and fruit area were all taken into account in this study. The dried fig dielectric constant was measured at six different frequency levels: 12, 22, 32, 42, 52, and 62 MHz. The best frequency was then chosen using the improved distance evaluation feature selection approach. Image processing was also used to determine the average diameter and area of the figures. Following that, the dielectric constant of the most effective frequency, the average diameter, and the area of the fruit were used as input parameters in the artificial neural network classification model to classify and describe the moisture and porosity level of the dried fig. The most essential dielectric constant information relating to moisture and porosity level was at frequencies of 22 and 52 MHz, respectively. Finally, classification accuracy of 95.7% for moisture and 91.3% for porosity level was attained. The results demonstrated the excellent performance and capabilities of the proposed approach for rating the internal quality of dried figs

A comprehensive review of Uniform Solar Illumination at Low Concentration Photovoltaic (LCPV) Systems

Conventional high performance silicon solar cells have a potential to generate more electricity by using low concentrating reflectors. Static solar concentrators reduce the cost of photovoltaic systems for given electrical power demand. However, non-uniform illumination on the conventional rectangular photovoltaic panel causes ohmic drops, mainly due to the cell that operates locally at higher irradiance. In this research study a comprehensive review has been carried out regarding Uniform Solar Illumination at Low Concentration Photovoltaic (LCPV) Systems. Another objective of the present study is therefore, calculating the pattern of sun incident at low concentration ratios for reflective troughs (V-type, cylindrical and compound parabolic concentrators) and linear Fresnel reflectors. The geometrical parameters of these concentrators were studied to obtain uniform illumination on the common rectangular photovoltaic panels. The designed concentrator with most uniform flux distribution, high concentration ratio and low requirement of mirror was fabricated and tested at ambient conditions. The optical simulation output of different concentrators illustrated the linear Fresnel reflector had uniform irradiance on the photovoltaic panel with standard deviation less than 30% of total income radiation. The experimental results showed that the linear Fresnel reflector has the potential to harvest more energy when using standard silicon solar cells in a basic concentration configuration. Finally thermal, electrical and total performances of a photovoltaic/thermal flat collector were measured with and without concentrator. Using the concentrator improved thermal and overall efficiency by 16% and 17.5% respectively. The maximum overall efficiency for PVT collector with concentrator and without concentrator was 91% and 78% respectively