Improving Plant Health Through Nutrient Remineralization in Aquaponic Systems.

The exploitation of readily bioavailable fish excreta as a source of plant nutrients lies at the cornerstone of aquaponics farming. Research on nutrient cycling in aquaponic systems has devoted considerable attention to the plant uptake of dissolved nutrients in fish excreta, however, the integration of particulate-bound nutrients into downstream hydroponic farming has remained elusive. The high amount of organic carbon present in fish sludge may lead to biofouling if directly incorporated into hydroponic circulation systems, reducing the utility of incorporating fish solids on a large scale. In this study, we implemented a novel treatment system capable of reducing the carbon and nitrogen load of fish solids to produce a liquid fertilizer for a downstream hydroponics unit. Lettuce (Lactuca sativa) fertilized with exclusively a commercial nutrient solution, the biofilter effluent (coupled aquaponic system), effluent from the solids treatment system, or the latter two combined were grown in nutrient flow technique gutters downstream of a recirculating aquaculture system stocked with rainbow trout (Oncorhynchus mykiss). While crop yields were lower for the aquaponic treatments compared to lettuce grown in a commercial nutrient solution, plant sap analysis demonstrated a contrasting picture with respect to internal nutrient concentrations. Lettuce grown in the commercial hydroponic solution were deficient in several mineral nutrients (Mg, Ca, Na, and Si) nor did they have higher iron concentrations despite the significantly higher EDTA-chelated aqueous iron (460 × greater than other treatments) in the nutrient solution. Nutrient uptake in the rhizosphere was not investigated on a molecular level, although stunted rhizosphere growth in the commercial nutrient solution control suggests a weakened capacity for nutrient uptake in comparison to other treatments. Alongside the remineralization of micronutrients, the solids treatment system addressed the common issue of excess carbon leading to biofouling via a total suspended solids reduction of 87.27% ± 9.95 during the coupled aquaponics cultivation period. Ultimately, these data lead to two important conclusions. Firstly, optimizing nutrient bioavailability is not synonymous to increasing the presence of a nutrient in the water column. Secondly, estimating ideal nutrient solution concentrations involves both preventing nutrient blocking and improving bioavailability.

Integrated Photonic System for Early Warning of Cyanobacterial Blooms in Aquaponics.

Cyanobacterial blooms produce hazardous toxins, deplete oxygen, and secrete compounds that confer undesirable organoleptic properties to water. To prevent bloom appearance, the World Health Organization has established an alert level between 500 and 2000 cells·mL-1, beyond the capabilities of most optical sensors detecting the cyanobacteria fluorescent pigments. Flow cytometry, cell culturing, and microscopy may reach these detection limits, but they involve both bulky and expensive laboratory equipment or long and tedious protocols. Thus, no current technology allows fast, sensitive, and in situ detection of cyanobacteria. Here, we present a simple, user-friendly, low-cost, and portable photonic system for in situ detection of low cyanobacterial concentrations in water samples. The system integrates high-performance preconcentration elements and optical components for fluorescence measurement of specific cyanobacterial pigments, that is, phycocyanin. Phycocyanin has demonstrated to be more selective to cyanobacteria than other pigments, such as chlorophyll-a, and to present an excellent linear correlation with bacterial concentration from 102 to 104 cell·mL-1 (R2 = 0.99). Additionally, the high performance of the preconcentration system leads to detection limits below 435 cells·mL-1 after 10 min in aquaponic water samples. Due to its simplicity, compactness, and sensitivity, we envision the current technology as a powerful tool for early warning and detection of low pathogen concentrations in water samples.

Comparative Performance Analysis of Support Vector Machine, Random Forest, Logistic Regression and k-Nearest Neighbours in Rainbow Trout (Oncorhynchus Mykiss) Classification Using Image-Based Features.

The main aim of this study was to develop a new objective method for evaluating the impacts of different diets on the live fish skin using image-based features. In total, one-hundred and sixty rainbow trout (Oncorhynchus mykiss) were fed either a fish-meal based diet (80 fish) or a 100% plant-based diet (80 fish) and photographed using consumer-grade digital camera. Twenty-three colour features and four texture features were extracted. Four different classification methods were used to evaluate fish diets including Random forest (RF), Support vector machine (SVM), Logistic regression (LR) and k-Nearest neighbours (k-NN). The SVM with radial based kernel provided the best classifier with correct classification rate (CCR) of 82% and Kappa coefficient of 0.65. Although the both LR and RF methods were less accurate than SVM, they achieved good classification with CCR 75% and 70% respectively. The k-NN was the least accurate (40%) classification model. Overall, it can be concluded that consumer-grade digital cameras could be employed as the fast, accurate and non-invasive sensor for classifying rainbow trout based on their diets. Furthermore, these was a close association between image-based features and fish diet received during cultivation. These procedures can be used as non-invasive, accurate and precise approaches for monitoring fish status during the cultivation by evaluating diet's effects on fish skin.