The quality of palm oil is strongly influenced by the maturity level of the fruit to be processed into palm oil. Many studies have been carried out for detecting and classifying the maturity level of oil palm fruit to improve the quality with the use of computer vision. However, most of these studies use datasets in the form of images of oil palm fresh fruit bunches (FFB) with incomplete categorization according to real conditions in palm oil mills. Therefore, this study introduces a new complete dataset obtained directly from palm oil mills in the form of videos and images with different categories in accordance with the real conditions faced by the grading section of the palm oil mill. The video dataset consists of 45 videos with a single category of FFB videos and 56 videos with a collection of FFB with multiple categories for each video. Videos are collected using a smart phone with a size of 1280 × 720 pixels with.mp4 format. In addition, this dataset has also been annotated and labelled based on the maturity level of oil palm fruit with 6 categories, which are unripe, under-ripe, ripe, overripe, empty bunches and abnormal fruit.© 2023. The Author(s).

Postharvest diseases and quality degradation are the major factors causing food losses in the fresh produce supply chain. Hence, detecting diseases and quality deterioration at the asymptomatic stage of produce enables growers to treat the diseases earlier, maintain quality and reduce postharvest food losses. With the emergence of numerous technologies to detect diseases early and monitor the quality of fresh produce, such as polymerase chain reaction, gas chromatography-mass spectrophotometry, and near-infrared spectroscopy, electronic nose (EN) has also gained acknowledgement and popularity in the past decade as a robust and non-invasive analysis tool to detect odor profile and establish volatile biomarkers for metabolomics databases. However, literature reviewing the EN research on the early detection of diseases in produce after harvest is scarce. The fundamental concept of EN working principles (odor sampling, gas detection, and data acquisition method), as well as the application of EN as a whole, are covered in the first section of the review. An in-depth discussion of the application of EN analysis in the early identification of postharvest diseases and quality monitoring is provided in the subsequent sections, which is the key objective of this comprehensive review. The prospect, limitations, and likely future developments of EN in the postharvest sector are further highlighted in the last section.© 2023 The Authors. Comprehensive Reviews in Food Science and Food Safety published by Wiley Periodicals LLC on behalf of Institute of Food Technologists.