Estimating Chlorophyll Content, Production, and Quality of Sugar Beet under Various Nitrogen Levels Using Machine Learning Models and Novel Spectral Indices

Elsayed, Salah; El-Hendawy, Salah; Elsherbiny, Osama; Okasha, Abdelaziz M.; Elmetwalli, Adel H.; Elwakeel, Abdallah E.; Memon, Muhammad Sohail; Ibrahim, Mohamed E. M.; Ibrahim, Hazem H.

doi:10.3390/agronomy13112743

Cited by 7 publications

(2 citation statements)

References 88 publications

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“…Each treatment combination was replicated 3 times, for a total of 60 stalks. Data were analyzed by ANOVA followed by Tukey's HSD test to determine significant differences between treatment means (p < 0.05) (Elsayed et al 2023;Elwakeel et al 2023b).…”

Section: Discussionmentioning

confidence: 99%

Some Engineering Factors Affecting the Performance of an Automatic Sugarcane Seed Cutting Machine

Elwakeel,

Nasrat,

Badawy

et al. 2024

Aswan University Journal of Environmental Studies

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The current study aimed to study some engineering factors affecting the performance of a new automatic sugarcane seed cutting machine based on an RGB color sensor and internet of things technology assisted by sugarcane seed monitoring and a counter-remote system. In the current study, we evaluated the performance of the developed machine using two saw knives with different tooth numbers (80 teeth and 30 teeth), five different cutting times (t1 = 1000 ms, t2 =1500 ms, t3 =2000 ms, t4 =2500 ms, and t5 =3000 ms), and four different sugarcane stalk diameters (d1 = 2.03 cm, d2 = 2.72 cm, d3 = 3.42 cm, and d4 = 3.94 cm). The obtained results showed that the invisible losses and cutting efficiency decreased with the decrease in the diameter of the sugarcane stalks, the increase in the cutting time, and the increase in the number of teeth of the rotary saw knife. While machine productivity followed the opposite trend. Where it decreased with increasing cutting time and increasing the diameter of the sugarcane stalks, while the number of teeth of the rotary saw knife had no significant difference.

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Section: Discussionmentioning

confidence: 99%

Some Engineering Factors Affecting the Performance of an Automatic Sugarcane Seed Cutting Machine

Elwakeel,

Nasrat,

Badawy

et al. 2024

Aswan University Journal of Environmental Studies

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“…Equations ( 8)-( 10) can be used to compute these parameters in accordance with [12,[77][78][79][80][81], 8)…”

Section: Mathematical Modeling Of Date Dryingmentioning

confidence: 99%

The Mathematical Modeling, Diffusivity, Energy, and Enviro-Economic Analysis (MD3E) of an Automatic Solar Dryer for Drying Date Fruits

Metwally,

Oraiath,

Elzein

et al. 2024

Sustainability

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Date fruit drying is a process that consumes a significant amount of energy due to the long duration required for drying. To better understand how moisture flows through the fruit during drying and to speed up this process, drying studies must be conducted in conjunction with mathematical modeling, energy analysis, and environmental economic analysis. In this study, twelve thin-layer mathematical models were designed utilizing experimental data for three different date fruit varieties (Sakkoti, Malkabii, and Gondaila) and two solar drying systems (automated solar dryer and open-air dryer). These models were then validated using statistical analysis. The drying period for the date fruit varieties varied between 9 and 10 days for the automated solar dryer and 14 to 15 days for open-air drying. The moisture diffusivity coefficient values, determined using Fick’s second law of diffusion model, ranged from 7.14 × 10−12 m2/s to 2.17 × 10−11 m2/s. Among the twelve thin-layer mathematical models, we chose the best thin drying model based on a higher R2 and lower χ2 and RMSE. The Two-term and Modified Page III models delivered the best moisture ratio projections for date fruit dried in an open-air dryer. For date fruit dried in an automated solar dryer, the Two-term Exponential, Newton (Lewis), Approximation diffusion or Diffusion Method, and Two-term Exponential modeling provided the best moisture ratio projections. The energy and environmental study found that the particular amount of energy used varied from 17.936 to 22.746 kWh/kg, the energy payback time was 7.54 to 7.71 years, and the net CO2 mitigation throughout the lifespan ranged from 8.55 to 8.80 tons. Furthermore, economic research showed that the automated solar dryer’s payback period would be 2.476 years.

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Advancing lettuce physiological state recognition in IoT aeroponic systems: A meta-learning-driven data fusion approach

Elsherbiny,

Gao,

et al. 2024

European Journal of Agronomy

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Estimating Chlorophyll Content, Production, and Quality of Sugar Beet under Various Nitrogen Levels Using Machine Learning Models and Novel Spectral Indices

Cited by 7 publications

References 88 publications

Some Engineering Factors Affecting the Performance of an Automatic Sugarcane Seed Cutting Machine

Some Engineering Factors Affecting the Performance of an Automatic Sugarcane Seed Cutting Machine

The Mathematical Modeling, Diffusivity, Energy, and Enviro-Economic Analysis (MD3E) of an Automatic Solar Dryer for Drying Date Fruits

Advancing lettuce physiological state recognition in IoT aeroponic systems: A meta-learning-driven data fusion approach

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