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

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