Agronomy · 2023 · 13 citations · 21 references
AgricultureAgricultural EngineeringPrecision AgricultureAgricultural Processing EngineeringNutrient ManagementPlant NutritionCoating TechnologyCoatingsControlled-release FertilizersFertilizer ManagementVariable Rate Fertilizer ApplicationWaste UtilizationCoating UniformityFilm ThicknessFluidized-bed Reactor DesignNutrient Release KineticsProcess ParametersProcess Parameters OptimizationSpray Application RateSpray Process Parameters
Controlled-release fertilizers are employed in precision agriculture to optimize technology-enabled farming without environmental deterioration. In this study, almond-shell lignocellulosic waste particles are chemically processed to synthesize a coating suspension for the production of controlled-release urea (CRU) in a Wurster fluidized-bed reactor. The interactive effect of process parameters such as atomizing air pressure (Pair), fluidized-bed temperature (Tfb), spray rate (Rspray), and fluidizing-air flow rate (Qair) on the (i) coating-film uniformity of CRU particles and (ii) longevity of nutrient-release from CRU is reported. CCRD is used with RSM to design the experiments for the optimization of nutrient-release performance and coating-film uniformity in terms of a coefficient-of-variance (CoV) of film thickness. The regression models indicate a good prediction of coating-film uniformity and nutrient-release time, with R2 = 0.971 and R2 = 0.98, respectively. The optimum conditions for coating-film uniformity are determined to be Pair = 3.5 bar, Tfb = 80 °C, Rspray = 0.15 mL/s, and Qair = 72 m3/h, with a predicted CoV film thickness of 11.5%. Similarly, Pair = 3.2 bar, Tfb = 78 °C, Rspray = 0.125 mL/s, and Qair = 75 m3/h are the optimum conditions for nutrient-release performance, with a prediction nutrient-release time = 56 h. The experimental validation yields a CoV of film thickness = 12.6% and a nutrient-release time = 49.5 h, indicating good agreement between predicted and experimental values. In addition, Tfb appears to be the most significant parameter.
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Understanding the potential applications of Artificial Intelligence in Agriculture Sector
Mohd Javaid, Abid Haleem, Ibrahim Haleem Khan et al. · Advanced Agrochem · 2022 · 777 citations · Full text
Agriculture, Agronomy, Crop Sciences +14
Helen Kendall, Beth Clark, Wenjing Li et al. · Precision Agriculture · 2021 · 118 citations · Full text
Agriculture, Precision Agriculture, Agricultural Development +12