4 research outputs found
Synthesis and VHDL Implementation of Fuzzy Logic Controller for Dynamic Voltage and Frequency Scaling (DVFS) Goals in Digital Processors
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Non-destructive in situ measurement of aquaponic lettuce leaf photosynthetic pigments and nutrient concentration using hybrid genetic programming
Phytopigment and nutrient concentration determination normally rely on laboratory chemical analysis. However, non-destructive and onsite measurements are necessary for intelligent closed environment agricultural systems. In this study, the impact of photosynthetic light treatments on aquaponic lettuce leaf canopy (Lactuca sativa var. Altima) was evaluated using UV-Vis spectrophotometry (300-800 nm), fourier transform infrared spectroscopy (4000-500 per cm), and the integrated computer vision and computational intelligence. Hybrid decision tree and multigene symbolic regression genetic programming (DT-MSRGP) exhibited the highest predictive accuracies of 80.9%, 89.9%, 83.5%, 85.5%, 81.3%, and 83.4% for chlorophylls a and b, β-carotene, anthocyanin, lutein, and vitamin C concentrations present in lettuce leaf canopy based on spectro-textural-morphological signatures. An increase in β-carotene and anthocyanin concentrations verified that these molecular pigments act as a natural sunscreen to protect lettuce from light stress and an increase in chlorophylls a and b ratio in the white light treatment corresponds to reduced emphasis on photon energy absorbance in chloroplast photosystem II. Red-blue light induces chlorophyll b concentration while white light promotes all other pigments and vitamin C. It was confirmed that the use of the DT-MSRGP model is essential as the concentration of phytopigment and nutrients significantly change during the head development and harvest stages. © 2021, Agriculture Faculty Brawijaya University. All rights reserved.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
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Adaptive fertigation system using hybrid vision-based lettuce phenotyping and fuzzy logic valve controller towards sustainable aquaponics
Sustainability is a major challenge in any plant factory, particularly those involving precision agriculture. In this study, an adaptive fertigation system in a three-tier nutrient film technique aquaponic system was developed using a non-destructive vision-based lettuce phenotype (VIPHLET) model integrated with an 18-rule Mamdani fuzzy inference system for nutrient valve control. Four lettuce phenes, that is, fresh weight, chlorophylls a and b, and vitamin C concentrations as outputted by the genetic programming-based VIPHLET model were optimized for each growth stage by injecting NPK nutrients into the mixing tank, as determined based on leaf canopy signatures. This novel adaptive fertigation system resulted in higher nutrient use efficiency (99.678%) and lower chemical waste emission (14.108 mg L-1) than that by manual fertigation (92.468%, 178.88 mg L-1). Overall, it can improve agricultural malpractices in relation to sustainable agriculture. © Fuji Technology Press Ltd.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]