Versatile method for grapevine row detection in challenging vineyard terrains using aerial imagery

Accurate detection and differentiation of grapevine canopies from other vegetation, along with individual grapevine row identification, pose significant challenges in precision viticulture (PV), especially within irregularly structured vineyards shaped by natural terrain slopes. This study employs aerial imagery captured by unmanned aerial vehicles (UAVs) and introduces an image processing methodology that relies on the orthorectified raster data obtained through UAVs. The proposed method adopts a data-driven approach that combines visible indices and elevation data to achieve precise grapevine row detection. Thoroughly tested across various vineyard configurations, including irregular and terraced landscapes, the findings underscore the method’s effectiveness in identifying grapevine rows of diverse shapes and configurations. This capability is crucial for accurate vineyard monitoring and management. Furthermore, the method enables clear differentiation between inter-row spaces and grapevine vegetation, representing a fundamental advancement for comprehensive vineyard analysis and PV planning. This study contributes to the field of PV by providing a reliable tool for grapevine row detection and vineyard feature classification. The proposed methodology is applicable to vineyards with varying layouts, offering a versatile solution for enhancing precision viticulture practices.