AI-Driven approaches in spice bioinformatics

Abstract

Technological breakthroughs like next-generation sequencing and mass spectrometry have generated vast datasets in spice crop science, but analyzing this data demands advanced computational approaches. This paper examines the transformative role of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), within spice bioinformatics. We highlight ML models, including support vector machines, random forests, and neural networks for detecting crop diseases and quantifying quality traits. We also explore DL architectures, such as convolutional and recurrent neural networks that autonomously extract meaningful patterns from complex, multi-modal data. While AI offers substantial benefits, challenges remain around limited datasets, annotation costs, and model interpretability. We propose strategies like transfer learning, explainable AI, and domain-informed feature extraction to address these issues.