Agricultural Image Analysis

Agricultural image analysis turns images and video into information about crop health, plant structure, pests, fruit quality, livestock, and field conditions, supporting monitoring and farm decisions. Common approaches include CNNs and YOLO detectors, U-Net and transformer segmenters, satellite time-series models, and learning from RGB, multispectral, hyperspectral, depth, or video data; newer work also adapts vision-language and foundation models. Researchers evaluate tasks such as disease classification, weed detection, fruit counting, phenotyping, and crop mapping, with self-supervised learning, synthetic data, and multimodal fusion used when labels or sensors are limited. A central challenge is that performance often drops across farms, species, seasons, and camera setups, so realistic cross-domain evaluation must be balanced against the need for efficient, reliable deployment at the field or device edge.