RSC Advances · 2021 · 18 citations · 22 references
AgricultureBiosystems EngineeringHydrologyMachine LearningNutrient ManagementStatistical LearningMineral AnalysisPhosphate MineralogyWater Resource MonitoringDeep Learning-based DiagnosisHydroponic SystemsKernel LearningKernel RegressionLearning KernelsMagnesium ConcentrationNutrient DetectionSoil Nutrient Diagnostics
respectively. Furthermore, the DKL was implemented for the first time in the third platform (LabVIEW) and deployed to determine three ions in a real on-site hydroponic system. The open architecture of the SDT allowed posting the measured results on a cloud computer. This would help growers monitor their plants' nutrients conveniently. The informative data about the three mentioned ions that have no commercial sensors so far, could be adapted to the other components to develop a fully automated fertigation system for hydroponic production.
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky et al. · 2014 · 34.3K citations
Artificial Intelligence and Statistics
Bin Yu, Karl Kumbier · arXiv (Cornell University) · 2017 · 358 citations · Full text
Natural Sciences, Computer Science, Artificial Intelligence +10
Hakima Zekki, Laurent Gauthier · Journal of the American Society for Horticultural Science · 1996 · 110 citations · Full text