AGRIDA-SSL: A DOMAIN-ADAPTIVE SELF-SUPERVISED LEARNING FRAMEWORK FOR ROBUST MULTI-CROP AGRICULTURAL IMAGE ANALYSIS ACROSS DIVERSE ENVIRONMENTS AND GROWTH STAGES

This work tackles the problem of domain shift in agricultural image analysis. Changes in crop types, growth stages, weather, and the devices used to capture images all cause the shift. We introduce a domain‑adaptive self‑supervised learning system called **AgriDA‑SSL**. It can use unlabeled images from several sources: satellites, UAVs (drones), and ground‑level cameras. The model combines two types of networks: a convolutional neural network (CNN) for local, pixel‑level details and a vision transformer (ViT) for broader, contextual information. During self‑supervised pre‑training we apply three tasks together: * contrastive representation learning, * masked image reconstruction, and * prototype‑guided clustering. These tasks help the system learn strong features that work across different

domains. To reduce the gap between data from different sensors, we add a domain‑consistency regularizer and use an adaptive data‑augmentation policy. We test the framework on several public datasets—PlantVillage, BigEarthNet, Agriculture‑Vision, and LUCAS—covering tasks such as crop classification, disease detection, field segmentation, and yield estimation. Results show high classification accuracy, precision, and F1‑score even when only a few labeled examples are available. This confirms that mixing complementary self‑supervised objectives with cross‑domain regularization yields a robust, label‑efficient solution for agricultural monitoring.

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