The structural heterogeneities of fruits and vegetables intensify the complexity to comprehend the interrelated physicochemical changes that occur during drying. Shrinkage of food materials during drying is a common physical phenomenon which affects the textural quality and taste of the dried product. The shrinkage of food material depends on many factors including material characteristics, microstructure, mechanical properties, and process conditions. Understanding the effect of these influencing factors on deformation of fruits and vegetables during drying is crucial to obtain better‐quality product. The majority of the previous studies regarding shrinkage are either experimental or empirical; however, such studies cannot provide a realistic understanding of the physical phenomena behind the material shrinkage. In contrast, theoretical modeling can provide better insights into the shrinkage that accompanies simultaneous heat and mass transfer during drying. However, limited studies have been conducted on the theoretical modeling of shrinkage of fruits and vegetables. Therefore, the main aim of this paper is to critically review the existing theoretical shrinkage models and present a framework for a theoretical model for the shrinkage mechanism. This paper also describes the effect of different drying conditions on material shrinkage. Discussions on how the diverse characteristics of fruits and vegetables affect shrinkage propagation is presented. Moreover, a comprehensive review of formulation techniques of shrinking models and their results are also presented. Finally, the challenges in developing a physics‐based shrinkage model are discussed.

Application of machine learning (ML)-based algorithms in food drying is an exciting and innovative approach to advance the drying technology. In order to appropriately develop this novel approach in all aspects of food drying field, significant scientific research is required. The main aspects of food drying research are the determination of material properties, microstructural characterization, mathematical modeling, and process optimization. It is essential to express this fundamental information through ML-based algorithms to advance the food drying research. This paper aims to present a comprehensive review of the application of machine learning-based approaches in food drying modeling, property prediction, microstructural characterization, and process parameters optimization. Moreover, this paper discusses the possibilities and challenges to apply ML-based algorithms in multiscale modeling and microwave-based hybrid drying. It is expected that this review paper will be beneficial in advancing the machine learning-based food drying technology.