Deep Learning-Based Classification of Olive Orchard Planting Systems Using High-Resolution Aerial Orthophotography: A Case Study in Spain

Global olive oil consumption continues to grow, and Spain, particularly Andalusia, holds the largest cultivation area within the EU, covering 1.68 million hectares, which expanded by 7.7% between 2013 and 2023. However, precise data on the planting systems used are lacking; this information is essential for assessing crop intensification and its environmental, economic, and social implications. This study applies Deep Learning (DL) techniques to classify the planting system of olive parcels previously identified in a national cadastral database, using high-resolution RGB imagery acquired from a nationwide public aerial orthophotography program. Five DL algorithms (VGG19, InceptionV3, MobileNet, ResNet50, and Xception) were compared across different planting-system classification schemes and spatial resolutions, using a dataset of 8000 images (2000 per class) divided into training (70%), validation (20%), and testing (10%) subsets. The best results were obtained with a three-class scheme (traditional, intensive, and super-intensive) at 0.5 m/pixel resolution, reaching precision values of 99.00% with MobileNet and 98.66% with VGG19. These results support extending the approach to larger areas for regional-scale monitoring of planting systems, including other Mediterranean olive-growing regions, and provide a basis for further research on the water use and labor demand associated with each planting system.