abstract
- The Agave tequilana crop (blue Weber agave) is of immense economic relevance to Mexico, which explains the large increase in agave-dedicated land in the country. This study focuses on the application of multispectral UAV-acquired photography for precise plant recognition to enable targeted interventions and optimize resource usage in blue agave cultivation. However, the scarcity of blue agave crop image datasets and the presence of shadows in images pose challenges. To address these issues, a comprehensive dataset of 932 diverse multispectral images was created, and a novel method based on computer vision and vegetation indices, such as the atmospherically resistant vegetation index (ARVI), was proposed for effective shadow attenuation and image segmentation. The proposed approach converts images to the HSV color space and uses an inverted shadow map to make corrections in the S and V channels, resulting in enhanced ARVI calculation and improved agave plant segmentation. Experimental evaluation demonstrates notable improvements across all metrics, with enhancements of up to 10% in certain cases. This study significantly contributes to the domain of agave cultivation through the development of a robust image-based analysis system and provides valuable insights for designing tailored methods to analyze plant life cycles. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.