Irrigation of arugula cultivars in the region of Zona da Mata Mineira / Irrigação de cultivares de rúcula na região da Zona da Mata Mineira

The region of the Zona da Mata of Minas Gerais stands out in the production of vegetables. To achieve the success in this activity, it is important to choose suitable cultivars and apply irrigation correctly. This study aimed to determine the optimum irrigation depth and evaluate new commercial arugula cultivars in the Zona da Mata of Minas Gerais. The cultivation was carried out in a greenhouse during three cycles with periods of 33 (January 11, 2016 to February 12, 2016), 36 (March 1, 2016 to April 5, 2016), and 36 (April 12, 2016 to May 17, 2016) days. The experimental design was a randomized complete block design with five replications, five irrigation depths (50, 75, 100, 125, and 150% of the crop evapotranspiration-ETc) in the plots, and three arugula cultivars (Antonella, Cultivada, and Folha Larga) in the subplots. A drip irrigation system was used. The parameters evaluated were root depth, root fresh mass, plant water potential, leaf temperature, number of commercial leaves, total number of plants, fresh shoot mass, and water use efficiency. Arugula cultivars did not present differences in their agronomic characteristics. An irrigation depth of the 50% ETc is recommended if the soil moisture is under the field capacity at the beginning of the arugula cycle.(AU)

A região da Zona da Mata mineira se destaca na produção de hortaliças. Para alcançar sucesso nesta atividade, é importante escolher cultivares adequadas e aplicar corretamente a irrigação. Objetivou-se determinar a lâmina ótima de irrigação e avaliar novas cultivares comerciais de rúcula na Zona da Mata de Minas Gerais. O cultivo foi em ambiente protegido durante três ciclos com períodos de 33 (11/1 à 12/2/2016), 36 (1/3 à 5/4/2016) e 36 (12/4 à 17/5/2016) dias. O delineamento experimental foi em blocos ao acaso em esquema de parcelas subdivididas, com cinco repetições, tendo nas parcelas cinco lâminas de irrigação (50; 75; 100; 125 e 150% da evapotranspiração da cultura-ETc) e nas subparcelas três cultivares de rúcula: Antonella, Cultivada e Folha Larga. O sistema de irrigação foi gotejamento. Os parâmetros avaliados foram: profundidade e massa fresca de raízes, potencial de água na planta, temperatura foliar, número de folhas comerciais, número total de plantas, massa fresca da parte aérea e eficiência do uso da água. As cultivares de rúcula não apresentaram diferenças em suas características agronômicas. Recomenda-se irrigar a cultura da rúcula com lâmina de 50% da ETc, desde que, no início do ciclo o solo esteja na capacidade de campo.(AU)

Irrigation of arugula cultivars in the region of Zona da Mata Mineira / Irrigação de cultivares de rúcula na região da Zona da Mata Mineira

The region of the Zona da Mata of Minas Gerais stands out in the production of vegetables. To achieve the success in this activity, it is important to choose suitable cultivars and apply irrigation correctly. This study aimed to determine the optimum irrigation depth and evaluate new commercial arugula cultivars in the Zona da Mata of Minas Gerais. The cultivation was carried out in a greenhouse during three cycles with periods of 33 (January 11, 2016 to February 12, 2016), 36 (March 1, 2016 to April 5, 2016), and 36 (April 12, 2016 to May 17, 2016) days. The experimental design was a randomized complete block design with five replications, five irrigation depths (50, 75, 100, 125, and 150% of the crop evapotranspiration-ETc) in the plots, and three arugula cultivars (Antonella, Cultivada, and Folha Larga) in the subplots. A drip irrigation system was used. The parameters evaluated were root depth, root fresh mass, plant water potential, leaf temperature, number of commercial leaves, total number of plants, fresh shoot mass, and water use efficiency. Arugula cultivars did not present differences in their agronomic characteristics. An irrigation depth of the 50% ETc is recommended if the soil moisture is under the field capacity at the beginning of the arugula cycle.

A região da Zona da Mata mineira se destaca na produção de hortaliças. Para alcançar sucesso nesta atividade, é importante escolher cultivares adequadas e aplicar corretamente a irrigação. Objetivou-se determinar a lâmina ótima de irrigação e avaliar novas cultivares comerciais de rúcula na Zona da Mata de Minas Gerais. O cultivo foi em ambiente protegido durante três ciclos com períodos de 33 (11/1 à 12/2/2016), 36 (1/3 à 5/4/2016) e 36 (12/4 à 17/5/2016) dias. O delineamento experimental foi em blocos ao acaso em esquema de parcelas subdivididas, com cinco repetições, tendo nas parcelas cinco lâminas de irrigação (50; 75; 100; 125 e 150% da evapotranspiração da cultura-ETc) e nas subparcelas três cultivares de rúcula: Antonella, Cultivada e Folha Larga. O sistema de irrigação foi gotejamento. Os parâmetros avaliados foram: profundidade e massa fresca de raízes, potencial de água na planta, temperatura foliar, número de folhas comerciais, número total de plantas, massa fresca da parte aérea e eficiência do uso da água. As cultivares de rúcula não apresentaram diferenças em suas características agronômicas. Recomenda-se irrigar a cultura da rúcula com lâmina de 50% da ETc, desde que, no início do ciclo o solo esteja na capacidade de campo.

Artificial neural network for prediction of the area under the disease progress curve of tomato late blight

Artificial neural networks (ANN) are computational models inspired by the neural systems of living beings capable of learning from examples and using them to solve problems such as non-linear prediction, and pattern recognition, in addition to several other applications. In this study, ANN were used to predict the value of the area under the disease progress curve (AUDPC) for the tomato late blight pathosystem. The AUDPC is widely used by epidemiologic studies of polycyclic diseases, especially those regarding quantitative resistance of genotypes. However, a series of six evaluations over time is necessary to obtain the final area value for this pathosystem. This study aimed to investigate the utilization of ANN to construct an AUDPC in the tomato late blight pathosystem, using a reduced number of severity evaluations. For this, four independent experiments were performed giving a total of 1836 plants infected with Phytophthora infestans pathogen. They were assessed every three days, comprised six opportunities and AUDPC calculations were performed by the conventional method. After the ANN were created it was possible to predict the AUDPC with correlations of 0.97 and 0.84 when compared to conventional methods, using 50 % and 67 % of the genotype evaluations, respectively. When using the ANN created in an experiment to predict the AUDPC of the other experiments the average correlation was 0.94, with two evaluations, 0.96, with three evaluations, between the predicted values of the ANN and they were observed in six evaluations. We present in this study a new paradigm for the use of AUDPC information in tomato experiments faced with P. infestans. This new proposed paradigm might be adapted to different pathosystems.

Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks

Germplasm classification by species requires specific knowledge on/of the culture of interest. Therefore, efforts aimed at automation of this process are necessary for the efficient management of collections. Automation of germplasm classification through artificial neural networks may be a viable and less laborious strategy. The aims of this study were to verify the classification potential of Capsicum accessions regarding/ the species based on morphological descriptors and artificial neural networks, and to establish the most important descriptors and the best network architecture for this purpose. Five hundred and sixty-four plants from 47 Brazilian Capsicum accessions were evaluated. Neural networks of multilayer perceptron type were used in order to automate the species identification through 17 morphological descriptors. Six network architectures were evaluated, and the number of neurons in the hidden layer ranged from 1 to 6. The relative importance of morphological descriptors in the classification process was established by Garson's method. Corolla color, corolla spot color, calyx annular constriction, fruit shape at pedicel attachment, and fruit color at mature stage were the most important descriptors. The network architecture with 6 neurons in the hidden layer is the most appropriate in this study. The possibility of classifying Capsicum plants regarding/ the species through artificial neural networks with 100 % accuracy was verified.

Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks

Germplasm classification by species requires specific knowledge on/of the culture of interest. Therefore, efforts aimed at automation of this process are necessary for the efficient management of collections. Automation of germplasm classification through artificial neural networks may be a viable and less laborious strategy. The aims of this study were to verify the classification potential of Capsicum accessions regarding/ the species based on morphological descriptors and artificial neural networks, and to establish the most important descriptors and the best network architecture for this purpose. Five hundred and sixty-four plants from 47 Brazilian Capsicum accessions were evaluated. Neural networks of multilayer perceptron type were used in order to automate the species identification through 17 morphological descriptors. Six network architectures were evaluated, and the number of neurons in the hidden layer ranged from 1 to 6. The relative importance of morphological descriptors in the classification process was established by Garson's method. Corolla color, corolla spot color, calyx annular constriction, fruit shape at pedicel attachment, and fruit color at mature stage were the most important descriptors. The network architecture with 6 neurons in the hidden layer is the most appropriate in this study. The possibility of classifying Capsicum plants regarding/ the species through artificial neural networks with 100 % accuracy was verified.(AU)

Artificial neural network for prediction of the area under the disease progress curve of tomato late blight

Artificial neural networks (ANN) are computational models inspired by the neural systems of living beings capable of learning from examples and using them to solve problems such as non-linear prediction, and pattern recognition, in addition to several other applications. In this study, ANN were used to predict the value of the area under the disease progress curve (AUDPC) for the tomato late blight pathosystem. The AUDPC is widely used by epidemiologic studies of polycyclic diseases, especially those regarding quantitative resistance of genotypes. However, a series of six evaluations over time is necessary to obtain the final area value for this pathosystem. This study aimed to investigate the utilization of ANN to construct an AUDPC in the tomato late blight pathosystem, using a reduced number of severity evaluations. For this, four independent experiments were performed giving a total of 1836 plants infected with Phytophthora infestans pathogen. They were assessed every three days, comprised six opportunities and AUDPC calculations were performed by the conventional method. After the ANN were created it was possible to predict the AUDPC with correlations of 0.97 and 0.84 when compared to conventional methods, using 50 % and 67 % of the genotype evaluations, respectively. When using the ANN created in an experiment to predict the AUDPC of the other experiments the average correlation was 0.94, with two evaluations, 0.96, with three evaluations, between the predicted values of the ANN and they were observed in six evaluations. We present in this study a new paradigm for the use of AUDPC information in tomato experiments faced with P. infestans. This new proposed paradigm might be adapted to different pathosystems.(AU)

Enxertia, produção e qualidade de tomateiros cultivados em ambiente protegido / Grafting, production and quality of tomato growth in protected environment

Com o objetivo de verificar os efeitos da enxertia na produção e qualidade de tomateiros cultivados em ambiente protegido, conduziu-se um experimento em Viçosa, Minas Gerais (MG). Seis tratamentos foram avaliados no delineamento em blocos casualizados, com três repetições, resultantes da combinação de duas cultivares de tomate 'Débora' e 'Sta. Clara', enxertadas sobre os porta-enxertos 'Anchor T' e 'BGH 3472', além das duas cultivares de pés francos. A enxertia foi realizada por encostia. Os tratamentos enxertados com 'BGH 3472' e os pés francos Débora e Sta. Clara apresentaram as maiores produtividades comerciais. Não houve variação no teor de SST dos frutos, e o pH da polpa dos frutos foi menor nas combinações 'Anchor T'/Sta. Clara e 'BGH 3472'/Débora. Observou-se maior ATT nos frutos de Débora e menor nos frutos de Sta. Clara. A relação SST/ATT foi maior nos frutos da combinação 'Anchor T'/Sta. Clara, comparados aos frutos das combinações 'BGH 3472'/Débora e 'Anchor T'/Débora.(AU)

In order to check the effects of grafting in the production and quality of tomato grown in unheated greenhouse, an experiment was conducted in Viçosa, Minas Gerais State, Brazil. Six treatments were evaluated in randomized block design with three replications, resulting from the combination of two tomato cultivars Débora and Sta. Clara, grafted on two rootstocks 'Anchor T' and 'BGH 3472' in addition of the two ungrafted cultivars. The grafting method utilized was used. The treatments with the rootstock 'BGH 3472' and the cultivars 'Débora' and 'Sta. Clara' presented greatest commercial yields. There was no change in the content of SST. Fruit pH was smaller at the combinations 'Anchor T'/Sta.Clara and 'BGH 3472'/Débora Clara. It was observed greater ATT in the pulp of the fruits of Débora and smaller in the fruits of Sta. Clara. SST/ATT ratio was greater in fruits of the combination 'Anchor T'/Sta.Clara when compared with the fruits of the combinations between BGH 3472/Débora and BGH and 'Anchor T'/Débora.(AU)