Systematic literature review approach on the fruit quality assessment based on fruit imaging techniques

Non-destructive quality assessment is one of the methods in image processing used to evaluate the qualities of fruits without destroying the internal structure and external appearance. Imaging processing techniques and machine learning methods have emerged as an effective way to evaluate fruit quality assessment, which helps to classify the fruit’s quality, especially during the harvest process. Image processing significantly shifts in fields, especially agriculture, medical, marketing profiling and vehicles. The rapid progress in agriculture continually increases the demand for up-to-date and accurate data to characterise the modality of imaging techniques used to evaluate the quality of fruits, which helps to guide and support research decisions, especially for future researchers that are new. This systematic literature review will focus on the type of fruits, type of modalities, pre-processing of imaging techniques and classification experiments that had been studied in recent years using planning, conducting and reporting methods. Through this study, 486 papers were selected at a preliminary stage and narrowed down to 35 papers that had been investigated from specialised research that has indicated the preferential types of data and regions of interest in image processing. In addition, imaging technique papers published between 2016 to 2022 have been studied, discussed and analysed. The presented finding outlines important research determination whose regardful report is of great value to this research community.