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This paper describes the Pacu Project innovation technologies during its trajectory related to aquaculture, as well as the impacts on its structure and dynamic. The research object is the Santa Rosa Farm production unit in Terenos, Mato Grosso do Sul. The influence of the interactive learning processes on the innovative act has been evaluated in this work. This case study uses the descriptive and explanatory methods used concerning the origin and dynamics of the company, through access to bibliographic and documentary sources, associated with data collection which was supported by a questionnaire applied to the executive manager, and visits to the unit. The research allowed the authors to verify the importance of the role played by interactive learning in the company, since its origins, be it either in processes of systemic innovation, as well as in the incorporation of competencies that enhance its resilience and the possibility of propagating knowledge systemically. [ABSTRACT FROM AUTHOR]

Fingerling counting in fish farming is still a problem to be solved, although there are some technological solutions used in experimental cases and controlled environments. In this paper, a fingerlings counter is evaluated using a new image database with 448 videos of the Pintado Real® fingerlings species. A total of 21,402 combinations of 6 parameters of the fingerlings counter were tested. In the training phase, 314 videos were randomly selected, which represent 70% of the database. The remaining 30%, corresponding to 134 videos, were used for testing. Focusing on the parameters that best recognize and track the fingerlings of Pintado Real®, it was obtained Pearson Coefficient of 0.9803 and a quadratic average error of 2.65 when comparing the manual and automatic counting. The results obtained six parameters sets that achieved these metrics, reaching higher performance on the fingerlings counter from a new image database. The image database used in our research is available for researchers. [ABSTRACT FROM AUTHOR]

• Using smartphones in aquiculture research for faster and more efficient processes. • Feasibility of using photos from smartphones in oocyte counting. • New approach based on the combination of SLIC and SVM for counting lambari oocytes. • Accuracy rate above 97% for counting oocytes from the Astyanax bimaculatus species. This work proposes a computer vision procedure for counting Twospot astyanax (Astyanax bimaculatus) oocytes in Petri dishes using images captured by smartphone. First, the proposed procedure uses simple linear iterative clustering (SLIC) to divide the images into groups of pixels (superpixels). Then, based on their color and space characteristics, the images are classified into light background, dark background, dirt, or oocyte by a machine learning algorithm. Five different types of machine learning algorithms were tested: support vector machines (SVM), decision trees using the algorithm J48 and random forest, k -nearest neighbors (k -NN), and Naive Bayes. To train the algorithms, 8.578 superpixels were classified by an expert into oocyte (n = 354), dirtiness (n = 651), dark background (n = 3.622), and light background (n = 3.951). Of the five learning algorithms, SVM obtained the best result with 97% correct oocyte recognition. Given the wide availability of smartphones, we therefore conclude that the presented procedure can be a valuable tool in future experiments and studies on fertilization and hatching success in Twospot astyanax. [ABSTRACT FROM AUTHOR]