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, environmental, 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 a 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 the Intersection over Union (IoU) value increased from 0.72 to 0.90 in the test set. The authors also propose a method for classifying agave crop maturity with 95% accuracy. With the resulting accurate models, agave production forecasting can be available for large regions. In addition, some supply-demand problems such as excessive supplies of agave or, deforestation, could be detected early. [ABSTRACT FROM AUTHOR]