Navegando por Autor "Botega, Gustavo Pucci"

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Sample size estimation of fruit maturation for Arabica’s coffee
(Instituto Agronômico (IAC), 2025-01-31) Botega, Gustavo Pucci; Abrahão, Juliana Costa de Rezende; Botelho, Thiago Tavares; Botelho, Cesar Elias; Salvador, Guilherme Soares; Gonçalves, Flávia Maria Avelar
This study aimed at establishing the ideal sample size for evaluating the maturation cycle in Coffea arabica, and investigating the errors associated with different sample sizes, in addition to verifying the possibility of using the clustering method to separate genotypes according to the maturation stage. Two experiments were analyzed: one with F2:3 progenies using visual maturation assessment through fruit counting, and another with cultivars using image processing for maturation assessment. To determine the ideal sample size for this trait, we used the estimation of the errors associated with maturation, using the bootstrap technique. Subsequently, the K-means algorithm was tested as an alternative for clustering genotypes into maturation classes. The application of the bootstrap technique in order to estimate the error associated with maturation revealed that the adoption of a 450-mL sample size resulted in an associated error of approximately 5%, indicating that it is an adequate size for character assessment. The implementation of K-means as a clustering tool offers a promising perspective for Arabica coffee plant breeding programs. A more comprehensive analysis, which not only assesses the proportion of ripe fruits, but also considers the distribution of different maturation stages, provides a more accurate understanding of the maturation process. This allows a more precise identification of genotypes with the most suitable performance for different growing conditions, as well as enabling adjustments in harvest management and post-harvest processing, optimizing coffee quality.
Visão computacional aplicada a análise de frutos de C. Arabica
(Universidade Federal de Lavras, 2023-03-16) Botega, Gustavo Pucci; Gonçalves, Flávia Maria Avelar
At coffee research centers, various traits are phenotyped by researchers. Some of these traits are directly determined by fruit phenotyping, such as ripening. Fruit ripening is an important trait to measure, because it allows for cultivars release with different ripening cycles, which is essential for farmers as it allows for the scaling of production and maximization of efficiency and profitability. However, measuring this trait in breeding programs presents several challenges. This study was divided into three chapters that present a comprehensive evaluation of coffee fruit and aspects associated with the selection and evaluation of ripening in Coffea arabica. In the first chapter, coffee fruits obtained from a phenotyping platform were thoroughly evaluated, by examining their morphological and color characteristics using computer vision. To achieve it, a classification model based on convolutional neural networks was created to classify the different stages of ripening. In the second chapter, images were synthesized from the generated image dataset to train a computer vision model based on the YOLO neural network architecture for direct classification and detection of coffee fruits in numerous scenarios and environments. In chapter 3, the objective was to establish the ideal sample size for ripening fruit evaluation and to verify the associated errors in adopting each sample size, as well as to demonstrate that the K-means clustering method can be an alternative to assist researchers in making decisions about the constituent genotypes of the breeding population. Detailed analysis was conducted on fruits from 21 cultivars, providing valuable information to researchers about their morphological and color characteristics. A total of 36.879 images of coffee fruits at different ripening stages were created. The use of the YOLO architecture allows for the direct evaluation of coffee fruits in different scenarios and environments, reducing and facilitating the process of phenotyping the trait. It was found that samples larger than 500 ml of fruits demonstrate an excellent sample size, and the use of the Kmeans technique to group data into different ripening cycles can be an excellent alternative for researchers, allowing for precise and efficient analysis.