Effects of methanolic macroalgae extracts from Caulerpa sertularioides and Ulva lactuca on Litopenaeus vannamei survival in the presence of Vibrio bacteria.

Macroalgae are potentially excellent sources of highly bioactive secondary metabolites that are useful for the development of new functional ingredients. This study was conducted to determine whether methanolic extracts from Caulerpa sertularioides and Ulva lactuca macroalgae might be possible alternatives for the prevention of shrimp vibriosis, which is caused by Vibrio parahaemolyticus and Vibrio alginolyticus. Macroalgae extracts prepared with methanol as the solvent were evaluated for antibacterial activity with the microplate method. The extracts' effects on the mortality of juvenile Litopenaeus vannamei were evaluated at doses of 150 and 300 mg L(-1). Two independent assays for V. parahaemolyticus and V. alginolyticus were performed. The methanolic extract of C. sertularioides exhibited activity against V. parahaemolyticus and V. alginolyticus, and it had minimal inhibitory concentrations of <1000 and < 1500 µg mL(-1), respectively. L. vannamei mortality in the presence of both The methanolic extract of C. sertularioides exhibited activity against V. parahaemolyticus and V. alginolyticus, and it had minimal inhibitory concentrations of <1000 and <1500 µg mL(-1), respectively. and V. alginolyticus bacteria significantly decreased after treatment with 300 mg L(-1) C. sertularioides methanolic extract.

Non-Invasive Fish Biometrics for Enhancing Precision and Understanding of Aquaculture Farming through Statistical Morphology Analysis and Machine Learning.

Aquaculture requires precise non-invasive methods for biomass estimation. This research validates a novel computer vision methodology that uses a signature function-based feature extraction algorithm combining statistical morphological analysis of the size and shape of fish and machine learning to improve the accuracy of biomass estimation in fishponds and is specifically applied to tilapia (Oreochromis niloticus). These features that are automatically extracted from images are put to the test against previously manually extracted features by comparing the results when applied to three common machine learning methods under two different lighting conditions. The dataset for this analysis encompasses 129 tilapia samples. The results give promising outcomes since the multilayer perceptron model shows robust performance, consistently demonstrating superior accuracy across different features and lighting conditions. The interpretable nature of the model, rooted in the statistical features of the signature function, could provide insights into the morphological and allometric changes at different developmental stages. A comparative analysis against existing literature underscores the competitiveness of the proposed methodology, pointing to advancements in precision, interpretability, and species versatility. This research contributes significantly to the field, accelerating the quest for non-invasive fish biometrics that can be generalized across various aquaculture species in different stages of development. In combination with detection, tracking, and posture recognition, deep learning methodologies such as the one provided in the latest studies could generate a powerful method for real-time fish morphology development, biomass estimation, and welfare monitoring, which are crucial for the effective management of fish farms.