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Qualidade pós-colheita de cagaitas armazenadas sob iluminação de Led em diferentes comprimentos de onda e tempos de exposição
(Universidade Estadual de Goiás, 2024-02-19)
Santos, Amanda Prager dos
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Campos, André José de
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Campos, André José de
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Morgado, Cristane Ascari
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Oliveira, Aryane Ribeiro
Cagaita (Eugenia dysenterica) is native fruit from the brazilian cerrado, with oval, ellipsoid or flat shape, fragile yellow skin and pulp with a pleasant, slightly acidic flavor. Like most fruits that have climateric respiratory pattern, cagaita has a high degree of perishability due to the high metabolic activity after harvest, in which the process of changing characteristics through ripening occurs accelerated, leading to the fast deterioration of the fruit. Therefore, it is necessary the application of appropriate techniques to delay post-harvest deterioration of these to minimize the losses. In this sense, in recent years, the use of artificial light during storage has been suggested to preserve or even improve nutritional properties of fruits and vegetables. This way, the objective of this study was to evaluate the post-harvest quality of cagaitas stored under LED (Light-emitting diodes) lighting at different wavelengths and times of exposure. For both experiments, a completely randomized design was used, in a 5x6 double factorial scheme, with the first factor consisted in 4 different LED colors (red, green, blue and white) and control (no light) for the first experiment, 5 times of exposure to green LED light (2, 4, 8, 12 and 24 hours) for the second experiment and the second factor composed of the days of post-harvest storage (0, 1, 2, 3, 4 and 5 days. The fruits were analyzed for: mass loss (PM), firmness, pH, titratable acidity (TA), soluble solids (SS), ripeness index (MI), color (L*, °Hue and Chroma), vitamin C, total extractable polyphenols (PET) and antioxidant activity by ABTS and DPPH methods. In the first experiment, the green LED light promoted a greater increase in the variables: soluble solids, luminosity, chroma, vitamin C and antioxidant activity using the DPPH method, in addition to causing greater maintenance in the pH parameter, promoting beneficial effects on the physical, physicochemical and bioactives from cagaita. And in the case of the second experiment, it was proven that the longer exposure time (continuous exposure to LED light) caused a greater increase in the luminosity, chroma, vitamin C and antioxidant activity parameters using the ABTS and DPPH methods. Futhermore, it is worth highlighting that studies on post-harvest storage of cerrado fruits are still scarce, and the use of LEDs in fruit storage in general is a recent study. Thus, the results of these experiments are the first steps for further research with cagaita associated with this technique where various conditions can be studied and optimized.
Estimativa de atributos químicos dos solos do Cerrado empregando redes neurais artificiais e o algoritmo floresta aleatória
(Universidade Estadual de Goiás, 2023-10-20)
Fonseca, Karina Azevedo
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Devilla, Ivano Alessandro
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Teixeira, Itamar Rosa
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Reis, Renata Cunha dos
A potato is considered one of the perishable vegetables, and increasing its shelf life is essential to expand its commercialization and improve post-harvest quality. In this regard, the study aimed to compare the drying of Asterix and Monalisa potato chips subjected to different drying temperatures in a forced-air circulation oven and a fluidized bed dryer. The experiment was conducted in a 2 x 2 x 4 factorial scheme, where treatments consisted of two drying methods (forced-air circulation oven and fluidized bed dryer), two potato varieties (Asterix and Monalisa), and four drying temperatures (35, 45, 55, and 65°C), with three replications. The mathematical models tested to predict the drying kinetics were: Two-term Exponential, Henderson and Pabis, Logarithmic, Newton, Page, Midilli, Thompson, Wang, and Singh. Analysis of Variance and comparison of means using Tukey's test at 5% significance level were applied to the obtained results, with the output variables being drying time and product moisture content. It was concluded that at 65°C, the fluidized bed drying method is more efficient for drying Asterix and Monalisa potato chips compared to forced-air circulation oven, taking 96.6 minutes for the chips to reach hygroscopic equilibrium, thus enhancing its applicability. For the selection of the mathematical model: For forced-air circulation oven drying, Asterix variety, the best-fitting models for temperatures ranging from 35 to 65°C were Wang and Singh, Logarithmic, and Henderson and Pabis, respectively, except for 45°C which did not achieve satisfactory adjustment. For the Monalisa variety, the mathematical model that fitted the temperatures were, at 50% of the temperatures Wang and Singh (35 and 45°C), and for the remaining temperatures Thompson and Midilli, respectively. As for the fluidized bed drying method, for the Asterix variety, the adjusted mathematical models were, at 35°C, no adjustment was observed, at 45°C Wang and Sing, and at 55 and 65°C Two-Term Exponential. For the Monalisa potato, the appropriate models for temperatures of 35 and 65°C were Wang and Sing, for 45°C Two-Term Exponential, and for 55°C Page.
Estimativa de atributos químicos dos solos do Cerrado empregando redes neurais artificiais e o algoritmo floresta aleatória
(Universidade Estadual de Goiás, 2023-08-31)
Souza e Silva, Jefferson
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Melo, Francisco Ramos de
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Moraes, Maria Joselma de
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Lima, José Leonardo Oliveira
Artificial Intelligence (AI) has played a significant role in globalization, profoundly impacting several sectors, including agriculture. In the agricultural context, AI, through techniques such as Artificial Neural Networks (ANNs) and Machine Learning, such as the Random Forest algorithm, has played a crucial role in optimizing agricultural processes, such as crop management, reducing costs and increased precision of operations. In this work, the application of Artificial Neural Networks (ANNs) and the Random Forest (RF) algorithm aimed to estimate soil chemical attributes, aiming to reduce the number of soil samples and compare the methodologies used in training to determine which one obtained the best results in the predictions. With this, a database was selected that contained soil samples with information on calcium (CA), pH, phosphorus (P), potassium (K), magnesium (MG), sum of calcium and magnesium (CA+MG), matter organic matter (MO) and cation exchange capacity (CEC). The study employed three distinct approaches: Model A, Model B and Model C. Models A and B used the Random Forest (RF) algorithm, while Model C employed Artificial Neural Networks (ANNs). Each of these models was trained and validated based on samples from the selected database. The comparative analysis of the models revealed that Model B presented promising results, evaluated using regression model performance metrics, such as the coefficient of determination (R²) and the mean squared error (EMQ). Therefore, it was possible to demonstrate that the application of AI, through techniques such as Artificial Neural Networks (ANNs) and the Random Forest algorithm, had a positive impact on the estimation of soil chemical attributes. Model B stood out as the most effective approach, delivering solid results based on performance assessment metrics.
Elaboração de filmes biodegradáveis de amidos de milho, batata, fruta-do-lobo, lírio-do-brejo e suas misturas aplicados como recobrimento em sementes de feijão comum (P. vulgaris L.)
(Universidade Estadual de Goiás, 2023-10-04)
Batista, Ana Maria Gomes
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Ascheri, Diego Palmiro Ramirez
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Teixeira, Itamar Rosa
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Faria, Raquel Cintra de
One of the research fronts for biodegradable materials focuses on obtaining materials from starch, as this is a promising polymer for preparing packaging due to its low cost and abundance. The production, characterization and application of starch-based films to cover seeds have been extensively studied from the most different sources, with positive results in the development of global agricultural production. The objective of this study was to evaluate the preparation of film-forming starch solutions, including alternative sources such as wolf fruit and marsh lily rhizome, compared to the most common sources corn, potato, and their respective mixtures, and its effects caused by the coating of common bean seeds on physiological quality. The following tests were evaluated to evaluate seed coverage: germination, accelerated aging, length and dry mass of seedlings. The variables rate and permeability to water vapor (Tva and Pva), tensile strength (RT) and Strength were not significant in any linear or quadratic adjustment model. Regarding the variables thickness and elongation (E), it was observed that the only model that presented significance to the data was the quadratic model. When covering the seeds, vigor was the best parameter for seed covering, with corn starch (T1), corn + wolf fruit (T5), corn + marsh lily (T6) and potato + fruit. do-lobo (T7) the treatments with the best results.
Aplicação de técnicas de inteligência artificial e dados de sensoriamento remoto por imagem digital para predição da produtividade de soja (Glycine Max L.) em região do Cerrado
(Universidade Estadual de Goiás, 2023-03-31)
Sousa, Diogo Santos
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Reis, Elton Fialho dos
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Melo, Francisco Ramos de
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Souza, João Mauricio Fernandes
Soybean (Glycine max L.) is one of the most important crops in the world's agricultural production and essential in the context of food security. The global demand for food is growing more and more, and the adoption of new technologies that allow this production to increase in a sustainable way is necessary. In this context, the objective of this work was to verify the potential of using artificial intelligence models to estimate soybean yields based on remote sensing (RS) data obtained through aerial and orbital platforms. For this study, an area of approximately 100 hectares cultivated with soybeans during the 2021/2022 harvest was used, which was subdivided into experimental plots, totaling a sample group of 5011 plots intended for the study. The spectral response of the crop canopy at different phenological stages was measured, and finally a yield survey was conducted with a grain harvester. These spectral data were combined, and together with vegetation indexes, used to predict soybean yields through Artificial Neural Network (ANN) models. Two ANN architectures were modeled, the first using data from the Planet Scope Satellite captured at five different phenological stages of the soybean crop, and the second from UAV data at the R5 stage. The fits of the models were evaluated for the plot of test data using the statistical indices of Pearson's correlation coefficient (r), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R²). For the prediction model based on satellite images, the best results found were in the R5 vegetative stage, with correlation values of 0.71, mean absolute error of 175.45 kg ha-1 representing a percentage error of 3.68% and R² of 0.5. As for modeling from UAV data, Pearson correlation values of 0.72, 172.94 kg ha-1 for MAE, representing an MAPE of 3.66%, and R² of 0.51, were found. Both models showed no tendency to cluster errors spatially, having a global Moran's index of around 0. The results indicate that the use of both satellite and UAV imagery, modeled by ANNs, can provide accurate and robust prediction of soybean yields.