In potato breeding, maturity class (MC) is a crucial selection criterion because this is a critical aspect of commercial potato production. Currently, the classification of potato genotypes into MCs is done visually, which is time- and labor-consuming. The objective of this research was to use vegetation indices (VIs) derived from unmanned aerial vehicle (UAV) imagery to remotely assign MCs to potato plants grown in trials, representing three different early stages within a multi-year breeding program. The relationships between VIs (GOSAVI - Green Optimized Soil Adjusted Vegetation Index, MCARI2 - Modified Chlorophyll Absorption Index-Improved, NDRE - Normalized Difference Red Edge, NDVI - Normalized Difference Vegetation Index, and OSAVI - Optimized Soil Adjusted Vegetation Index and WDVI - Weighted Difference Vegetation Index) and visual potato canopy status were determined. Further, this study aimed to identify factors that could improve the accuracy (decrease Mean Absolute Error - MAE) of potato MC estimation remotely. Results show that VIs derived from UAV imagery can be effectively used to remotely assign MCs to potato breeding lines, with higher accuracy for the potato B-clones (20 plants per plot) than the A-clones (6 plants per plot). Among the tested VIs, the NDRE allowed for potato MC evaluation with the lowest MAE. Applying NDRE for remote MC estimation using a validation dataset of potato B-clones (100 plants per plot), resulted in an MC estimate with a 0.81 MAE. However, the accuracy of potato MC estimation using UAV image-based methods should be improved by reducing the potato canopy's variability (increasing uniformity) within the plot. This could be achieved by minimizing 1) potato vines bending over the neighboring row, causing vine overlap between plots, and 2) plants damaged by tractor wheels during field operations. En el mejoramiento de la papa, la clase de madurez (CM) es un criterio de selecci & oacute;n crucial porque este es un aspecto cr & iacute;tico de la producci & oacute;n comercial de papa. Actualmente, la clasificaci & oacute;n de los genotipos de papa en MC se realiza visualmente, lo que requiere mucho tiempo y trabajo. El objetivo de esta investigaci & oacute;n fue utilizar & iacute;ndices de vegetaci & oacute;n (VIs) derivados de im & aacute;genes de veh & iacute;culos a & eacute;reos no tripulados (UAV) para asignar de forma remota MCs a plantas de papa cultivadas en ensayos, representando tres etapas tempranas diferentes dentro de un programa de mejoramiento de varios a & ntilde;os. Se determinaron las relaciones entre los VIs (GOSAVI - & Iacute;ndice de Vegetaci & oacute;n Ajustado al Suelo Optimizado Verde, MCARI2 - & Iacute;ndice de Absorci & oacute;n de Clorofila Modificado-Mejorado, NDRE - Borde Rojo de Diferencia Normalizada, NDVI - & Iacute;ndice de Vegetaci & oacute;n de Diferencia Normalizada, y OSAVI - & Iacute;ndice de Vegetaci & oacute;n Ajustado al Suelo Optimizado y WDVI - & Iacute;ndice de Vegetaci & oacute;n de Diferencia Ponderada) y la visualizaci & oacute;n del dosel de la papa. Adem & aacute;s, este estudio tuvo como objetivo identificar factores que podr & iacute;an mejorar la precisi & oacute;n (disminuir el Error Absoluto Medio - MAE) de la estimaci & oacute;n de MC de papa de forma remota. Los resultados muestran que los VI derivados de las im & aacute;genes de UAV se pueden utilizar de manera efectiva para asignar MC de forma remota a las l & iacute;neas de mejoramiento de papa, con mayor precisi & oacute;n para los clones B de papa (20 plantas por parcela) que para los clones A (6 plantas por parcela). Entre los VI probados, el NDRE permiti & oacute; la evaluaci & oacute;n de la MC de papa con el MAE m & aacute;s bajo. La aplicaci & oacute;n de NDRE para la estimaci & oacute;n remota de MC utilizando un conjunto de datos de validaci & oacute;n de clones B de papa (100 plantas por parcela), result & oacute; en una estimaci & oacute;n de MC con un MAE de 0.81. Sin embargo, la precisi & oacute;n de la estimaci & oacute;n de la MC de la papa utilizando m & eacute;todos basados en im & aacute;genes UAV debe mejorarse reduciendo la variabilidad del dosel de la papa (aumentando la uniformidad) dentro de la parcela. Esto podr & iacute;a lograrse minimizando 1) los tallos de papa que se doblan sobre el surco vecino, lo que causa la superposici & oacute;n de follaje entre las parcelas, y 2) las plantas da & ntilde;adas por las ruedas de los tractores durante las operaciones de campo.

The proposed system integrates various services into a common platform for digital agriculture, linking various IoT sensor nodes distributed in the field and connected via LoRaWAN technology to collect soil and climate data that will be processed using a kappa Big Data architecture to display the data collected by the sensors in real-time and provide control and monitoring of tomato crop through notifications to the farmer via a mobile application. In addition, the system offers the ability to detect tomato diseases, using an image-based classification model. This model is able to detect leaf diseases with an accuracy of 86%. The goal is to provide farmers with an accurate view of their crops and mitigate disease and environmental damage.