Continual learning for agrifood: A systematic review

Continual learning (CL) has emerged as an important machine learning paradigm for developing adaptive artificial intelligence systems capable of life long learning, while addressing neural network challenges such as catastrophic forgetting. In this article, we provide a systematic review of the applications of CL in the agrifood sector. We organize our review by first introducing the fundamental principles of CL and specifying the methodology adopted for the definition of the relevant queries, the retrieval of the papers in the selected sources and the analysis of the literature. We then present and describe existing CL approaches applied to agrifood systems, identifying trends and challenges. Through our extensive literature survey, we highlight main characteristics of CL approaches in the context of agriculture, discussing current views and future perspectives.