Ground-based active optical sensors (GBAOS) have been successfully used in agriculture to predict crop yield potential (YP) early in the season and to improvise N rates for optimal crop yield. However, the models were found weak or inconsistent due to environmental variation especially rainfall. The objectives of the study were to evaluate if GBAOS could predict YP across multiple locations, soil types, cultivation systems, and rainfall differences. This study was carried from 2011 to 2013 on corn (Zea mays L.) in North Dakota, and in 2017 in potatoes in Maine. Six N rates were used on 50 sites in North Dakota and 12 N rates on two sites, one dryland and one irrigated, in Maine. Two active GBAOS used for this study were GreenSeeker and Holland Scientific Crop Circle Sensor ACS 470 (HSCCACS-470) and 430 (HSCCACS-430). Rainfall data, with or without including crop height, improved the YP models in term of reliability and consistency. The polynomial model was relatively better compared to the exponential model. A significant difference in the relationship between sensor reading multiplied by rainfall data and crop yield was observed in terms of soil type, clay and medium textured, and cultivation system, conventional and no-till, respectively, in the North Dakota corn study. The two potato sites in Maine, irrigated and dryland, performed differently in terms of total yield and rainfall data helped to improve sensor YP models. In conclusion, this study strongly advocates the use of rainfall data while using sensor-based N calculator algorithms.
ZnS thin films were grown onto glass substrates by flash evaporation technique, the effects of ? – rays on the optical constants of ZnS these films were studied. It was found that ? – rays affected all the parameters under investigation.
In this work, Pure and Cu: doped titanium dioxide nano-powder was prepared through a solid-state method. the dopant concentration [Cu/TiO2 in atomic percentage (wt%)] is derived from 0 to 7 wt.%. structural properties of the samples performed with XRD revealed all nanopowders are of titanium dioxide having polycrystalline nature. Physical and Morphological studies were conducted using a scanning electronic microscope SEM test instrument to confirm the grain size and texture. The other properties of samples were examined using an optical microscope, Lee's Disc, Shore D hardness instrument, Fourier-transform infrared spectroscopy (FTIR), and Energy-dispersive X-ray spectroscopy (EDX). Results showed that the thermal conductivity
... Show MoreTin Oxide (SnO2) films have been deposited by spray pyrolysis technique at different substrate temperatures. The effects of substrate temperature on the structural, optical and electrical properties of SnO2 films have been investigated. The XRD result shows a polycrystalline structure for SnO2 films at substrate temperature of 673K. The thickness of the deposited film was of the order of 200 nm measured by Toulansky method. The energy gap increases from 2.58eV to 3.59 eV when substrate temperature increases from 473K to 673K .Electrical conductivity is 4.8*10-7(.cm)-1 for sample deposited at 473K while it increases to 8.7*10-3 when the film is deposited at 673K
PbxCd1-xSe compound with different Pb percentage (i.e. X=0,
0.025, 0.050, 0.075, and 0.1) were prepared successfully. Thin films
were deposited by thermal evaporation on glass substrates at film
thickness (126) nm. The optical measurements indicated that
PbxCd1-xSe films have direct optical energy gap. The value of the
energy gap decreases with the increase of Pb content from 1.78 eV to
1.49 eV.
In this work, the optical properties of Cu2S with different thickness
(1400, 2400, 4400) Ǻ have been prepared by chemical spray pyrolys
is method onto clean glass substrate heated at 283 oC ±2. The effect
of thickness on the optical properties of Cu2S has been studied. It
was found that the optical properties of the electronic transitions on
fundamental absorption edge were direct allowed and the value of the
optical energy gap of Cu2S (Eg) for direct transition decreased from
(2.4-2.1) eV with increasing of the thickness from (1400 - 4400)Ǻ
respectively. Also it was found that the absorption coefficient is
increased with increasing of thicknesses. The optical constants such<
This study proposed using color components as artificial intelligence (AI) input to predict milk moisture and fat contents. In this sense, an adaptive neuro‐fuzzy inference system (ANFIS) was applied to milk processed by moderate electrical field‐based non‐thermal (NP) and conventional pasteurization (CP). The differences between predicted and experimental data were not significant (