Abstract: One of the biggest challenges that breeders face is the development of improved cultivars in changing climate conditions posing extra challenges to their labor. On the other hand, the availability of data generated with automated systems offers an opportunity to characterize genetically and phenotypically genotypes with high detail. Modern sequencing technologies delivering hundreds of thousands of molecular makers, offered the opportunity of selecting genotypes without the need of observing these in fields and this methodology was coined as Genomic Selection (GS). More recently, sophisticated automated phenotyping platforms depending on sensors able to measure a large number of plant features were also developed and have shown potential in plant breeding applications. These modern phenotyping systems that attempt to efficiently deliver phenotypic information on secondary traits are also know as high-throughput phenotyping platforms (HTPPs). The integration of HTPP with GS models opened a new research front to improve the efficiency of the selection methods based on genomic data only, specially of those traits depending on a large number of genes with small effects (complex traits). However, there are still remaining some issues to solve for developing a robust methodology able to combine in an efficient and informed way these two high dimensional data types. In this document, we provide an overview of the statistical analysis of the data derived of the HTTPs for improving the predictive ability of conventional GS models. We provide a brief introduction showing the utility of genomic data in plant breeding applications. After, we provide an overview of the field-based HTPPs considering the light detection and ranging and the unmanned aerial vehicles and how the image data derived from these platforms can be used to accelerate genetic gains. After that, we discuss about the extension of the conventional GS models to allow the incorporation of data derived of the HTPPs as main effects and also in interaction with environmental factors. The availability of several sources of information have opened a venue to investigate besides the univariate or single trait model, models based on multiple traits and also models that consider multiple time measures allowing longitudinal GS studies. Finally, we provide some conclusions as well as we mention some the current issues that do not allow to fully exploit the potential of HTTPs in plant breeding applications.

The development of an integrated breeding field book (IBFieldbook) for different crops involves the generation, handling and analysis of large amounts of data. Managing the integration of environmental, pedigree, and phenotypic information for plant breeding data analyses requires appropriate and successful software that facilitates breeders, technicians, and researchers management of the vast collected field information in an easy, efficient and interactive way. Users may also need methods to exchange information with different devices used to record information in the field. Additionally, collected information needs to be analyzed inside or outside the application, and then generate reports for germplasm improvement.

El desarrollo de un sistema integrado de libros de campo para mejoramiento (IBFieldbook, por sus siglas en inglés) para diferentes cultivos incluye la generación, manejo y análisis de grandes volúmenes de datos. El manejo de integración de la información fenotípica, ambiente y pedigrí para análisis de datos requiere de un programa computacional ("software") apropiado y exitoso, que facilite a los mejoradores, técnicos e investigadores la administración de la información recolectada en el campo de manera fácil, eficiente e interactiva. Los usuarios del programa también necesitan métodos para intercambiar información con diferentes dispositivos usados para almacenar información en el campo. La información recolectada necesita además ser analizada adentro y afuera de la aplicación, y generar reportes para el mejoramiento del germoplasma.