  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.
