Plant disease detection models can achieve impressive results when trained and tested within the same dataset, but real-world agricultural environments are far more diverse. Different crops, disease patterns, imaging conditions, and data distributions can make it difficult for a model trained on one crop to generalise effectively to another.

This session explores cross-crop transfer learning as a practical approach toward more generalizable agricultural AI. Using deep learning-based crop disease detection as a case study, the presentation examines how knowledge learned from one crop can be transferred to another, the role of pretrained lightweight architectures, and the challenges introduced by differences between agricultural domains.

The talk presents experimental insights from a perennial-crop disease detection pipeline, including transfer learning with MobileNetV2 and the development of a broader Cross-Perennial Domain Adaptation (CPDA) direction. It discusses model initialization, feature reuse, selective fine-tuning, dataset-shift challenges, and evaluation strategies for assessing whether transferred knowledge is genuinely useful.

Rather than treating high accuracy on a single dataset as sufficient evidence of generalization, the session highlights why cross-domain validation is essential for building agricultural AI systems that can move beyond controlled datasets toward practical deployment.

The session is intended for AI/ML students, researchers, computer-vision practitioners, and professionals interested in transfer learning, domain adaptation, and sustainable AI applications. Basic familiarity with machine learning and deep learning is recommended.