A comprehensive review of fruit and vegetable classification techniques

TL;DR: The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images.

TL;DR: This article presents a review of the use of CNN applied to different automatic processing tasks of fruit images: classification, quality control, and detection, and observes that in the last two years (2019–2020), theUse of CNN for fruit recognition has greatly increased obtaining excellent results.

TL;DR: An outdoor machine vision system was developed with a low-cost Kinect V2 sensor to improve the accuracy of apple detection by filtering the background objects using depth features, and results indicated that the use of a depth filter to remove background trees improved fruit detection accuracy by 2.5%.

TL;DR: A novel method to apply the RGB-D (Red Green Blue–Depth) sensors and fuse aligned RGB and NIR images with deep convolutional neural networks (CNN) for fruit detection and indicated that the proposed kiwifruit detection approach shows a potential for better fruit detection.

TL;DR: The authors proposed a human-interpretable vocabulary of texture attributes to describe common texture patterns, complemented by a new describable texture dataset for benchmarking, and showed that these have excellent efficiency and generalization properties if the convolutional layers of a deep model are used as filter banks.

TL;DR: The multifractal spectrum (MFS) is introduced, a new texture signature that is invariant under the bi-Lipschitz map, which includes view-point changes and non-rigid deformations of the texture surface, as well as local affine illumination changes.

TL;DR: A hybrid classification method based on fitness-scaled chaotic artificial bee colony (FSCABC) algorithm and feedforward neural network (FNN) was seen to be effective in classifying fruits.

TL;DR: A unified approach that can combine many features and classifiers that requires less training and is more adequate to some problems than a naive method, where all features are simply concatenated and fed independently to each classification algorithm.