The aim of this study was to identify maize haploid plants and compare the efficiency of identification of maize haploid plants using the R1-nj morphological marker, plant vigor, flow cytometry, chromosome counting, and microsatellite molecular markers under tropical conditions. We also established a protocol for chromosome duplication in maize haploid plants. Fourteen

The goal of this study was to verify the influence of the population effect in the estimates of genetic and phenotypic components and to identify the best soybean progeny or lines in a commercial soybean breeding programme. We evaluated 292 populations for grain yield and absolute maturity during three agricultural years. To quantify the efficiency of the inclusion of the population's effect in the model, we estimated genetic gain with the selection, the Spearman's correlation, the coincidence index, the realized gain and correlated response of selected genotypes with and without the effect of the population. It was found that the variance components, heritability and coefficient of experimental variation were better estimated when the effect of the population was included, providing greater gain with selection for grain yield and absolute maturity. Coincidence and ranking among the selected progeny with and without the effect of the population are of greater magnitude in more advanced inbreeding generations and at higher percentage of selected progeny. The use of the population effect has greater importance in earlier generations of inbreeding.

Early blight is one of the most important fungal diseases of potato. The objective of this study was to evaluate the in vitro reaction of potato genotypes to the severity of early blight and compare the species

The development and recommendation of single cross maize hybrids (SH) to be used in extensive land areas (mega-environments), and in different crop seasons requires many experiments under numerous environmental conditions. The question we asked is if the data from these multi-environment experiments are sufficient to identify the best hybrid combinations. The aim of this study was to critically analyze the phenotype data of experiments of yield, established by a large seed producing company, under a high level of imbalance. Data from evaluation of 2770 SH were used from experiments conducted over four years, involving the first and second crop seasons, in 50 locations of different years and regions of Brazil. Different types of analysis were carried out and genetic and non-genetic components were estimated, with emphasis on the different interactions of the SH with the environments. Results showed that the coincidence of common hybrids in these experiments is normally small. The estimates of the correlations between of the hybrids coinciding in the environments two by two is of low magnitude. The hybrid × crop season interaction was always expressive; however, the interactions of hybrids and other environmental variables were also important. Under these conditions, alternatives were discussed for making with the information obtained from the experiments, can be more efficient on the process to obtain new hybrids by companies.

Fresh market sweet corn (Zea mays L.) is a row crop commercialized as a vegetable, resulting in strict expectations for ear size, color, and shape. Ear phenotyping in breeding programs is typically done manually and can be subjective, time consuming, and unreliable. Computer vision tools have enabled an inexpensive, high‐throughput, and quantitative alternative to phenotyping in agriculture. Here we present a computer vision tool using open‐source Python and OpenCV to measure yield component and quality traits relevant to sweet corn from photographs. This tool increases accuracy and efficiency in phenotyping through high‐throughput, quantitative feature extraction of traits typically measured qualitatively. EarCV worked in variable lighting and background conditions, such as under full sun and shade and against grass and dirt backgrounds. The package compares ears in images taken at varying distances and accurately measures ear length and ear width. It can measure traits that were previously difficult to quantify such as color, tip fill, taper, and curvature. EarCV allows users to phenotype any number of ears, dried or fresh, in any orientation while tolerating some debris and silk noise. The tool can categorize husked ears according to the predefined USDA quality grades based on length and tip fill. We show that the information generated from this computer vision approach can be incorporated into breeding programs by analyzing hybrid ears, capturing heritability of yield component traits, and detecting phenotypic differences between cultivars that conventional yield measurements cannot. Ultimately, computer vision can reduce the cost and resources dedicated to phenotyping in breeding programs.