Bayesian neural network for plant disease detection: A role of prior knowledge in data-scarce agricultural systems

Plant diseases are a major threat to global food security, causing an estimated annual economic loss of approximately USD 220 billion. Even though deep learning models excel at image-based disease detection, the requirement for many labelled images with a quantitative prediction uncertainty can be problematic in real-world agricultural applications. Bayesian neural networks (BNNs) overcome these limitations by incorporating prior knowledge from plant pathology, symptom morphology and environmental factors within a probabilistic learning framework. This approach not only improves prediction accuracy but also yields calibrated uncertainty estimates alongside each prediction. This review synthesises recent studies on BNNs, focusing on domain-knowledge, data-driven and functional priors for plant disease detection. The evidence gained from the studies reviewed indicates that BNNs consistently outperform deterministic models, especially in low-data regimes, as may be the case for smallholder farming characterised by heterogeneous environments and limited annotation resources. The review also highlights challenges related to computational complexity, prior selection and edge deployment, while outlining future research opportunities for robust agricultural artificial intelligence (AI) systems.