The desorption isotherms and thermodynamic properties of two cultivars of sweet corn were obtained during the drying process of these products. The isotherms were determined by a dynamic method for various temperature and humidity conditions. Equilibrium moisture content (X eq ) data were correlated by the Guggenheim-Anderson-de Boer model and an artificial neural network (ANN) model. These models were fitted to the experimental data. The X eq for corn grain increased with an increase in the relative humidity at fixed temperature and decreased with an increase in temperature at a constant relative humidity. The experimental data were analysed by a thermodynamic approach to obtain the isosteric heat of desorption (DH), differential entropy (DS), activation energy (E a ) and Gibbs free energy (DG). The DH and DS increased with a decrease in moisture content, while DG decreased exponentially with an increase in X eq . The Arrhenius equation was used to obtain E a values, with Supersweet corn having higher E a .

The desorption isotherms and thermodynamic properties of coffee from different processing stages were obtained during the drying process of this product. The isotherms were determined by a static gravimetric method for various temperature and humidity conditions. Equilibrium moisture content (M e ) data were correlated by several mathematical models and an artificial neural network (ANN) model. The M e for coffee fruits, pulped coffee and green coffee increased with an increase in the water activity (a w ) at any particular temperature. At a constant a w , coffee fruits samples had higher M e than the remaining coffee samples. Based on statistical parameters, the ANN model, modified Henderson and GAB models were adequate to describe the sorption characteristics of the samples. Isosteric heat of sorption was evaluated by means of the Clausius-Clapeyron equation. It decreased with increasing moisture content. The coffee fruits had higher isosteric heat of sorption than that pulped coffee and green coffee.

Fertilizer application at variable rates requires dense sampling to determine the resulting field spatial variability. Defining management zones is a technique that facilitates the variable-rate application of agricultural inputs. The apparent electrical conductivity of the soil is an important factor in explaining the variability of soil physical-chemical properties. Thus, the objective of this study was to define management zones for coffee (Coffea Arabica L.) production fields based on spatial variability of the apparent electrical conductivity of the soil. The resistivity method was used to measure the apparent soil electrical conductivity. Soil samples were collected to measure the chemical and physical soil properties. The maps of spatial variability were generated using ordinary kriging method. The fuzzy k-means algorithm was used to delimit the management zones. To analyze the agreement between the management zones and the soil properties, the kappa coefficients were calculated. The best results were obtained for the management zones defined using the apparent electrical conductivity of the soil and the digital elevation model. In this case, the kappa coefficient was 0.45 for potassium, which is an element that is associated with quality coffee. The other variable that had a high kappa coefficient was remaining phosphorous; the coefficient obtained was 0.49. The remaining phosphorus is an important parameter for determining which fertilizers and soil types to study.

ABSTRACT. Mechanical harvesting can be considered an important factor to reduce the costs in coffee production and to improve the quality of the final product. Coffee harvesting machinery uses mechanical vibrations to accomplish the harvesting. Therefore, the determination of the natural frequencies of the fruit-stem systems is an essential dynamic parameter for the development of mechanized harvesting system by mechanical vibrations. The objective of this study was to develop a three-dimensional finite element model to determine the natural frequencies and mode shapes of the coffee fruit-stem systems, considering different fruit ripeness. Moreover, it was carried out a theoretical study, using the finite element three-dimensional model, based on the linear theory of elasticity, for determining the generated stress in a coffee fruit-stem system, during the harvesting process by mechanical vibration. The results showed that natural frequencies decrease as the ripeness condition of the fruit increases. Counter-phase mode shape can provide better detachment efficiency considering the stress generation on coffee fruit-stem system during the harvesting by mechanical vibrations and presented a difference greater than 40 Hz between the natural frequencies of the green and ripe fruit.Keywords: coffee harvesting, mechanization, mechanical vibrations.Simulação do comportamento dinâmico do sistema fruto-pedúnculo do café empregando o método de elementos finitos RESUMO. A colheita mecanizada pode ser considerada como um importante fator na redução de custos de produção e na obtenção de café de qualidade. Um dos princípios mais difundidos e empregados em máquinas colhedoras de frutos é o de vibrações mecânicas. Logo, a determinação das frequências naturais dos sistemas fruto-pedúnculo é requisito básico para o desenvolvimento de sistemas de colheita por vibrações mecânicas. O objetivo desse trabalho foi o desenvolvimento de um modelo tridimensional em elementos finitos do sistema fruto-pedúnculo, para a determinação das frequências naturais e modos de vibração para os diferentes graus de maturação do sistema frutopedúnculo. Adicionalmente, foi realizado um estudo teórico para a determinação dos esforços gerados em um sistema fruto-pedúnculo quando submetido a vibrações mecânicas. Os resultados mostraram que as frequências naturais diminuem à medida que o grau de maturação dos frutos aumenta. O modo de vibração em contra-fase pode proporcionar melhor eficiência de derriça por gerar níveis de tensões mais acentuados na união entre o fruto e o pedúnculo, pela sua configuração geométrica. Para o modo de vibração em contra-fase o intervalo entre as frequências naturais para os graus de maturação verde e cereja foi superior a 40 Hz.Palavras-chave: colheita de café, mecanização, vibrações mecânicas.

Aboveground biomass (AGB) data are important for profitable and sustainable pasture management. In this study, we hypothesized that vegetation indexes (VIs) obtained through analysis of moderate spatial resolution satellite data (Landsat‐8 and Sentinel‐2) and meteorological data can accurately predict the AGB of Brachiaria (syn. Urochloa) pastures in Brazil. We used AGB field data obtained from pastures between 2015 and 2019 in four distinct regions of Brazil to evaluate (i) the relationship between three different VIs—normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2) and optimized soil adjusted vegetation index (OSAVI)—and meteorological data with pasture aboveground fresh biomass (AFB), aboveground dry biomass (ADB) and dry‐matter content (DMC); and (ii) the performance of simple linear regression (SLR), multiple linear regression (MLR) and random forest (RF) algorithms for the prediction of pasture AGB based on VIs obtained through satellite imagery combined with meteorological data. The results highlight a strong correlation (r) between VIs and AGB, particularly NDVI (r = 0.52 to 0.84). The MLR and RF algorithms demonstrated high potential to predict AFB (R2 = 0.76 to 0.85) and DMC (R2 = 0.78 to 0.85). We conclude that both MLR and RF algorithms improved the biomass prediction accuracy using satellite imagery combined with meteorological data to determine AFB and DMC, and can be used for Brachiaria (syn. Urochloa) AGB prediction. Additional research on tropical grasses is needed to evaluate different VIs to improve the accuracy of ADB prediction, thereby supporting pasture management in Brazil.