Fish production faces various challenges throughout its cycle, from rearing to consumption. Organic acids have emerged as an effective fish feed and meat treatment solution. They promote health and well-being, control pathogens, improve digestion, and contribute to food preservation. This study was therefore carried out to evaluate the evolution of publications on the use of organic acids in aquaculture over time, identifying the leading journals, authors, countries, and relevant organizations associated with the publications and determining the keywords most used in publications and research trends on this type of accommodation using bibliometric analysis. For this analysis, the Web of Science (WoS) and Scopus databases were used, with the keywords and Boolean operators “organic acid*” AND (“pathogens” OR “microorganism*” OR “bacteria” OR “fungi”) AND (“fish” OR “fry” OR “pisciculture”). Ninety-six articles were found in 44 journals, with the participation of 426 authors and 188 institutions, from 1995 to 2024. The most crucial publication source with the highest impact factor was the journal Aquaculture, with 14 articles, 2 of which were written by the most relevant author, Koh C., who received the highest number of citations and had the highest impact factor among the 426 authors. China had the most scientific production, with 26 publications on organic acids in aquaculture. However, Malaysia was the country that published the most cited documents, a total of 386. The most relevant affiliation was the University of Sains Malaysia, which participated in the publication of eight articles. The 10 most frequent keywords were fish, organic acids, citric acid, article, bacteria, growth, microorganisms, Oncorhynchus mykiss, animals, and digestibility. The results indicate increased publications on the benefits of using organic acids in aquaculture, highlighting their effectiveness as antibacterial agents and promoters of zootechnical development. However, gaps still require more in-depth research into the ideal dosages, mechanisms of action, and long-term impacts of these compounds.

Aquaculture produces more than 122 million tons of fish globally. Among the several economically important species are the Serrasalmidae, which are valued for their nutritional and sensory characteristics. To meet the growing demand, there is a need for automation and accuracy of processes, at a lower cost. Convolutional neural networks (CNNs) are a viable alternative for automation, reducing human intervention, work time, errors, and production costs. Therefore, the objective of this work is to evaluate the efficacy of convolutional neural networks (CNNs) in counting round fish fingerlings (Serrasalmidae) at different densities using 390 color photographs in an illuminated environment. The photographs were submitted to two convolutional neural networks for object detection: one model was adapted from a pre-trained CNN and the other was an online platform based on AutoML. The metrics used for performance evaluation were precision (P), recall (R), accuracy (A), and F1-Score. In conclusion, convolutional neural networks (CNNs) are effective tools for detecting and counting fish. The pre-trained CNN demonstrated outstanding performance in identifying fish fingerlings, achieving accuracy, precision, and recall rates of 99% or higher, regardless of fish density. On the other hand, the AutoML exhibited reduced accuracy and recall rates as the number of fish increased.

Identifying and counting fish are crucial for managing stocking, harvesting, and marketing of farmed fish. Researchers have used convolutional networks for these tasks and explored various approaches to enhance network learning. Batch normalization is one technique that improves network stability and accuracy. This study aimed to evaluate machine learning for identifying and counting pirapitinga Piaractus brachypomus fry with different batch sizes. The researchers used one thousand photographic images of Pirapitinga fingerlings, labeled with bounding boxes. They trained the adapted convolutional network model with batch normalization layers added at the end of each convolution block. They set the training to one hundred and fifty epochs and tested batch sizes of 5, 10, and 20. Furthermore, they measured network performance using precision, recall, and mAP@0.5. Models with smaller batch sizes performed less effectively. The training with a batch size of 20 achieved the best performance, with a precision of 96.74%, recall of 95.48%, mAP@0.5 of 97.08%, and accuracy of 98%. This indicates that larger batch sizes improve accuracy in detecting and counting pirapitinga fry across different fish densities.

The aim of this study was to develop a mathematical model to predict the body weight of tilapia fingerlings using variables obtained through computer vision. A total of 2092 tilapia fingerlings and juveniles, weighing between 10 and 100 g, were fasted for 12 h, anesthetized, weighed, and photographed using an iPhone 12 Pro Max at 33 cm height in a closed container with different bottom colors. Images were segmented using Roboflow’s instance segmentation model, achieving 99.5% mean average precision, 99.9% precision, and 100% recall. From the segmented images, area, perimeter, major axis (MA), minor axis (SA), X and Y centroids, compactness, eccentricity, and the MA/SA ratio were extracted. Seventy percent of the data was used to build the model, and 30% for validation. Stepwise multiple regression (backward selection) was performed, using body weight as the dependent variable. The prediction model was: −17.7677 + 0.0007539(area) – 0.0848303 (MA) – 0.108338(SA) + 0.0034496(CX). The validation model showed similar coefficients and R² = 0.99. The second validation, using observed versus predicted values, also yielded an R² of 0.99 and a mean absolute error of 1.57 g. Correlation and principal component analyses revealed strong positive associations among body weight, area, axes, and predicted values. Computer vision proved effective for predicting tilapia fingerlings’ weight.

A produção de tilápias encontra-se dentre as atividades da piscicultura que mais crescem mundialmente. A alimentação encontra-se entre os principais fatores que impactam no custo de produção representando cerca de 70 a 80% em sistemas de criação intensivos, sendo a proteína o nutriente mais caro da dieta. Com isso, a aplicação de fontes alternativas que diminuam os custos de produção de ração torna-se relevantes para a cadeia produtiva de peixes. Os grãos secos de destilaria com solúveis, com sigla em inglês DDGS (Dried Distillers Grains With Solubles), são coprodutos obtidos pelo processo fermentativo do etanol e podem ser empregados em dietas de animais. Seu uso na alimentação de tilápias começou a ser explorado como uma fonte alternativa para substituição do farelo de soja ou farinha de peixe na ração, baseado nisso objetivou-se com essa revisão de literatura fazer uma abordagem do uso do DDGS em dietas para tilápias sob o ponto de vista do desempenho produtivo, respostas imunológicas e morfologia intestinal.

O cerrado é o segundo maior bioma do Brasil, sendo um pilar da agricultura brasileira, com uma enorme biodiversidade do mundo. Compreendendo diversos tipos de vegetação incluindo várias espécies de plantas medicinais estudadas pela etnofarmacologia, buscando cada vez mais plantas medicinais e seus agentes biologicamente ativos. Uma dessas plantas é a Aspidosperma olivaceum conhecida por Guatambu. Uma planta muito usada no conhecimento popular para tratamentos de doenças infecciosas parasitarias, antioxidantes, anti-inflamatórios e hipolipemiantes. São reconhecidas numerosas causas de hiperlipemia e grande parte das complicações desse desequilíbrio bioquímico está nas cardiopatias ateromatosas. A ciência sempre está em busca de novas estratégias farmacológicas para o equilíbrio fisiológico do colesterol. Aproximadamente 250 alcaloides indólicos já foram isolados no Guatambu. É estudado tanto para doenças como Leishmaniose até a redução de níveis de colesterol plasmático. É provável que esta espécie de planta seja útil no desenvolvimento de um medicamento hipocolesterolêmico, mas a sua avaliação farmacológica ainda é necessária. Sendo assim o presente estudo utilizou-se de metodologia de revisão literária, por meio de pesquisa bibliográfica. Concluiu-se que o Guatambu, é o exemplar vegetal do bioma cerrado mais reconhecido entre a população rural e acadêmica para o tratamento das dislipidemias devido seus princípios ativos como a guatambuina e DL50 serem promissores para os novos estudos e formulações de formas farmacêuticas para a ampliação de seu uso na população.