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.

翻译：负责任且可持续的龙舌兰-龙舌兰酒生产链对于墨西哥龙舌兰产区的社会、环境及经济发展至关重要。因此，开发大规模自动监测龙舌兰产区的新工具具有现实意义。本研究利用甚高分辨率卫星影像实现了蓝龙舌兰（Agave tequilana Weber azul）作物分割与成熟度分类，能够为此类监测任务提供支持。针对龙舌兰作物分割特有的深度学习实际难题，例如数据匮乏、标注质量低、数据高度不平衡及模型性能不佳等问题，我们提出了超越传统数据增强与迁移学习的解决方案：将主动学习与人工监督下的合成图像生成相结合。实验结果表明，测试集上基于交并比（IoU）评估的分割性能从0.72提升至0.90。此外，我们提出的龙舌兰作物成熟度分类方法准确率达到95%。基于这些高精度模型，可实现大区域龙舌兰产量预测，并能早期识别供应过剩或森林砍伐等供需失衡问题。