Crop yield prediction is a critical measurement, especially in the time when parts of the world are suffering from farming issues. Yield forecasting gives an alert regarding economic trading, food production monitoring, and global food security. This research was conducted to investigate whether active optical sensors could be utilized for potato (Solanum tuberosum L.) yield prediction at the mid.le of the growing season. Three potato cultivars (Russet Burbank, Superior, and Shepody) were planted and six rates of N (0, 56, 112, 168, 224, and 280 kg ha−1), ammonium sulfate, which was replaced by ammonium nitrate in the 2nd year, were applied on 11 sites in a randomized complete block design, with four replications. Normalized difference vegetation index (NDVI) and chlorophyll index (CI) measurements were obtained weekly from the active optical sensors, GreenSeeker (GS) and Crop Circle (CC). The 168 kg N ha−1 produced the maximum potato yield. Indices measurements obtained at the 16th and 20th leaf growth stages were significantly correlated with tuber yield. Multiple regression analysis (potato yield as a dependent variable and vegetation indices, NDVI and CI, as independent variables) could make a remarkable improvement to the accuracy of the prediction model and increase the determination coefficient. The exponential and linear models showed a better fit of the data. Soil organic matter content increased the yield significantly but did not affect the prediction models. The 18th and 20th leaf growth stages are the best time to use the sensors for yield prediction.
GaN thin films were deposited by thermal evaporation onto
glass substrates at substrate temperature of 403 K and a thickness of
385 nm . GaN films have amorphous structure as shown in X-ray
diffraction pattern . From absorbance data within the range ( 200-
900 ) nm direct optical energy gap was calculated . Also the others
optical parameters like transmittance T, reflectance R , refractive
index n , extinction coefficient k , real dielectric constant 1 Î , and
imaginary dielectric constant 2 Î were determined . GaN films
have good absorbance and minimum transmittance in the region of
the visible light .
Chalcogenide glasses SeTe have been prepared from the high purity constituent elements .Thin films of SeTe compound have been deposited by thermal evaporation onto glass substrates for different values of film thickness . The effect of varying thickness on the value of the optical gap is reported . The resultant films were in amorphous nature . The transmittance spectra was measured for that films in the wavelength range (400-1100) nm . The energy gap for such films was determined .
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In this work the design and construction of optical pumping system was presented. The parameters of the pumping source to obtain discharge current density sufficient to shift the flash lamp spectrum towards uv portion of spectrum were measured.The current density was supplied to the flash lamp must be greater than 4000Amp./cm2 to obtain the spectral range wavelength lies between 0.2 and 0.35?m. The current density was obtained by a capacitor 50?F, at 7KV discharge voltage. The applied electrical energy to the flash lamp was more than 1200 J, and the current density was around 5000 Amp./cm2.The electrical parameters of the flash lamp were calculated. The impedance parameters(K0) from the voltage and the peak current pulse was measured in ran
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Abstract
The aim of the current research is to identify the effect of the effective reading strategy on the achievement of second-middle students in biology, as well as the imaginative thinking skills of students. The researcher adopted the experimental design with partial control to achieve the goal of the research. The current research community identified the second-middle students in the government schools of the Baghdad Education Directorate / Rusafa I for the academic year (2021-2022 AD). The Safina Intermediate School for Girls was chosen to be the research sample in the form of intentionally, two classes were randomly selected from a total of four classes, one of them is experimental and the other is con
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The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system
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