Paradigmas de aprendizaje automático aplicados a la teledetección: imágenes RGB e imágenes multiespectrales.

213 p.La tendencia actual en el uso de sensores para recopilar datos georreferenciados con una alta redundancia, se basa en la aplicación de métodos robustos y automatizados para extraer información geoespacial. Los resultados derivan en un cambio de paradigmas en tecnologías geoespaciales, que hasta este momento no han generado un límite en su aplicación. Sumado a ello, los avances en tecnologías sobre ordenadores, aprendizaje máquina, detección de patrones y visión computacional muestran una clara tendencia a la generación de estudios avanzados sobre imágenes, lo cual impulsa a la investigación de la información geoespacial con un progreso exponencial.El presente trabajo realiza un recorrido sobre paradigmas de aprendizaje automático aplicados en imágenes aéreas (RGB) y satelitales (multiespectrales), metodologías que han sido aplicadas en campo con interesantes resultados

Paradigmas de aprendizaje automático aplicados a la teledetección: imágenes RGB e imágenes multiespectrales.

213 p.La tendencia actual en el uso de sensores para recopilar datos georreferenciados con una alta redundancia, se basa en la aplicación de métodos robustos y automatizados para extraer información geoespacial. Los resultados derivan en un cambio de paradigmas en tecnologías geoespaciales, que hasta este momento no han generado un límite en su aplicación. Sumado a ello, los avances en tecnologías sobre ordenadores, aprendizaje máquina, detección de patrones y visión computacional muestran una clara tendencia a la generación de estudios avanzados sobre imágenes, lo cual impulsa a la investigación de la información geoespacial con un progreso exponencial.El presente trabajo realiza un recorrido sobre paradigmas de aprendizaje automático aplicados en imágenes aéreas (RGB) y satelitales (multiespectrales), metodologías que han sido aplicadas en campo con interesantes resultados

Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery

The responsible and sustainable agave-tequila production chain is fundamental for the social, environment and economic development of Mexico's agave regions. It is therefore relevant to develop new tools for large scale automatic agave region monitoring. In this work, we present an Agave tequilana Weber azul crop segmentation and maturity classification using very high resolution satellite imagery, which could be useful for this task. To achieve this, we solve real-world deep learning problems in the very specific context of agave crop segmentation such as lack of data, low quality labels, highly imbalanced data, and low model performance. The proposed strategies go beyond data augmentation and data transfer combining active learning and the creation of synthetic images with human supervision. As a result, the segmentation performance evaluated with Intersection over Union (IoU) value increased from 0.72 to 0.90 in the test set. We also propose a method for classifying agave crop maturity with 95% accuracy. With the resulting accurate models, agave production forecasting can be made available for large regions. In addition, some supply-demand problems such excessive supplies of agave or, deforestation, could be detected early.Comment: 12 pages, 8 figure

A Multi-Disciplinary Approach to Remote Sensing through Low-Cost UAVs

The use of Unmanned Aerial Vehicles (UAVs) based on remote sensing has generated low cost monitoring, since the data can be acquired quickly and easily. This paper reports the experience related to agave crop analysis with a low cost UAV. The data were processed by traditional photogrammetric flow and data extraction techniques were applied to extract new layers and separate the agave plants from weeds and other elements of the environment. Our proposal combines elements of photogrammetry, computer vision, data mining, geomatics and computer science. This fusion leads to very interesting results in agave control. This paper aims to demonstrate the potential of UAV monitoring in agave crops and the importance of information processing with reliable data flow.We wish to acknowledge the Consejo Nacional de Ciencia y Tecnologia (CONACYT) for its financial support to the PhD studies of Gabriela Calvario. We are grateful to Cubo Geoespacial S.A .de C.V. and special to Ing. Jordan Martinez for the stimulus to this work, more information about this Company is available at: http://www.cubogeoespacial.com/. In addition, we are grateful to the support of the Tequila Regulatory Council (CRT), which has allowed us to monitor several crops. This paper has been supported by the Spanish Ministerio de Economia y Competitividad, contract TIN2015-64395-R (MINECO/FEDER, UE), as well as by the Basque Government, contract IT900-16. This work was also supported in part by CONACYT (Mexico), Grant 258033

An Agave Counting Methodology Based on Mathematical Morphology and Images Acquired through Unmanned Aerial Vehicles

Blue agave is an important commercial crop in Mexico, and it is the main source of the traditional mexican beverage known as tequila. The variety of blue agave crop known as Tequilana Weber is a crucial element for tequila agribusiness and the agricultural economy in Mexico. The number of agave plants in the field is one of the main parameters for estimating production of tequila. In this manuscript, we describe a mathematical morphology-based algorithm that addresses the agave automatic counting task. The proposed methodology was applied to a set of real images collected using an Unmanned Aerial Vehicle equipped with a digital Red-Green-Blue (RGB) camera. The number of plants automatically identified in the collected images was compared to the number of plants counted by hand. Accuracy of the proposed algorithm depended on the size heterogeneity of plants in the field and illumination. Accuracy ranged from 0.8309 to 0.9806, and performance of the proposed algorithm was satisfactory.This research was supported by the Spanish Ministerio de Economía y Competitividad, contract TIN2015-64395-R (MINECO/FEDER, UE), as well as by the Basque Government, contract IT900-16. This work was also supported in part by CONACYT (Mexico), grant 258033