A comprehensive review of fruit and vegetable classification techniques

Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-of-the-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable

Review of deep learning methods in robotic grasp detection

For robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged to securely grab an object and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods have driven robotics researchers to explore the use of deep learning methods in task-generalised robotic applications. This paper reviews the current state-of-the-art in regards to the application of deep learning methods to generalised robotic grasping and discusses how each element of the deep learning approach has improved the overall performance of robotic grasp detection. Several of the most promising approaches are evaluated and the most suitable for real-time grasp detection is identified as the one-shot detection method. The availability of suitable volumes of appropriate training data is identified as a major obstacle for effective utilisation of the deep learning approaches, and the use of transfer learning techniques is proposed as a potential mechanism to address this. Finally, current trends in the field and future potential research directions are discussed

Class distribution-aware adaptive margins and cluster embedding for classification of fruit and vegetables at supermarket self-checkouts

The complex task of vision based fruit and vegetables classification at a supermarket self-checkout poses significant challenges. These challenges include the highly variable physical features of fruit and vegetables i.e. colour, texture shape and size which are dependent upon ripeness and storage conditions in a supermarket as well as general product variation. Supermarket environments are also significantly variable with respect to lighting conditions. Attempting to build an exhaustive dataset to capture all these variations, for example a dataset of a fruit consisting of all possible colour variations, is nearly impossible. Moreover, some fruit and vegetable classes have significant similar physical features e.g. the colour and texture of cabbage and lettuce. Current state-of-the-art classification techniques such as those based on Deep Convolutional Neural Networks (DCNNs) are highly prone to errors resulting from the inter-class similarities and intra-class variations of fruit and vegetable images. The deep features of highly variable classes can invade the features of neighbouring similar classes in a learned feature space of the DCNN, resulting in confused classification hyper-planes. To overcome these limitations of current classification techniques we have proposed a class distribution-aware adaptive margins approach with cluster embedding for classification of fruit and vegetables. We have tested the proposed technique for cluster-based feature embedding and classification effectiveness. It is observed that introduction of adaptive classification margins proportional to the class distribution can achieve significant improvements in clustering and classification effectiveness. The proposed technique is tested for both clustering and classification, and promising results have been obtained

A review of current neuromorphic approaches for vision, auditory, and olfactory sensors

Conventional vision, auditory, and olfactory sensors generate large volumes of redundant data and as a result tend to consume excessive power. To address these shortcomings, neuromorphic sensors have been developed. These sensors mimic the neuro-biological architecture of sensory organs using aVLSI (analog Very Large Scale Integration) and generate asynchronous spiking output that represents sensing information in ways that are similar to neural signals. This allows for much lower power consumption due to an ability to extract useful sensory information from sparse captured data. The foundation for research in neuromorphic sensors was laid more than two decades ago, but recent developments in understanding of biological sensing and advanced electronics, have stimulated research on sophisticated neuromorphic sensors that provide numerous advantages over conventional sensors. In this paper, we review the current state-of-the-art in neuromorphic implementation of vision, auditory, and olfactory sensors and identify key contributions across these fields. Bringing together these key contributions we suggest a future research direction for further development of the neuromorphic sensing field

Texture-based latent space disentanglement for enhancement of a training dataset for ANN-based classification of fruit and vegetables

The capability of Convolutional Neural Networks (CNNs) for sparse representation has significant application to complex tasks like Representation Learning (RL). However, labelled datasets of sufficient size for learning this representation are not easily obtainable. The unsupervised learning capability of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) provide a promising solution to this issue through their capacity to learn representations for novel data samples and classification tasks. In this research, a texture-based latent space disentanglement technique is proposed to enhance learning of representations for novel data samples. A comparison is performed among different VAEs and GANs with the proposed approach for synthesis of new data samples. Two different VAE architectures are considered, a single layer dense VAE and a convolution based VAE, to compare the effectiveness of different architectures for learning of the representations. The GANs are selected based on the distance metric for disjoint distribution divergence estimation of complex representation learning tasks. The proposed texture-based disentanglement has been shown to provide a significant improvement for disentangling the process of representation learning by conditioning the random noise and synthesising texture rich images of fruit and vegetables

A sample weight and adaboost CNN-based coarse to fine classification of fruit and vegetables at a supermarket self-checkout

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The physical features of fruit and vegetables make the task of vision-based classification of fruit and vegetables challenging. The classification of fruit and vegetables at a supermarket self-checkout poses even more challenges due to variable lighting conditions and human factors arising from customer interactions with the system along with the challenges associated with the colour, texture, shape, and size of a fruit or vegetable. Considering this complex application, we have proposed a progressive coarse to fine classification technique to classify fruit and vegetables at supermarket checkouts. The image and weight of fruit and vegetables have been obtained using a prototype designed to simulate the supermarket environment, including the lighting conditions. The weight information is used to change the coarse classification of 15 classes down to three, which are further used in AdaBoost-based Convolutional Neural Network (CNN) optimisation for fine classification. The training samples for each coarse class are weighted based on AdaBoost optimisation, which are updated on each iteration of a training phase. Multi-class likelihood distribution obtained by the fine classification stage is used to estimate a final classification with a softmax classifier. GoogleNet, MobileNet, and a custom CNN have been used for AdaBoost optimisation, with promising classification results

Challenges and solutions for autonomous ground robot scene understanding and navigation in unstructured outdoor environments: A review

The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges

An investigation into spike-based neuromorphic approaches for artificial olfactory systems

The implementation of neuromorphic methods has delivered promising results for vision and auditory sensors. These methods focus on mimicking the neuro-biological architecture to generate and process spike-based information with minimal power consumption. With increasing interest in developing low-power and robust chemical sensors, the application of neuromorphic engineering concepts for electronic noses has provided an impetus for research focusing on improving these instruments. While conventional e-noses apply computationally expensive and power-consuming data-processing strategies, neuromorphic olfactory sensors implement the biological olfaction principles found in humans and insects to simplify the handling of multivariate sensory data by generating and processing spike-based information. Over the last decade, research on neuromorphic olfaction has established the capability of these sensors to tackle problems that plague the current e-nose implementations such as drift, response time, portability, power consumption and size. This article brings together the key contributions in neuromorphic olfaction and identifies future research directions to develop near-real-time olfactory sensors that can be implemented for a range of applications such as biosecurity and environmental monitoring. Furthermore, we aim to expose the computational parallels between neuromorphic olfaction and gustation for future research focusing on the correlation of these senses

Adaptive use of thresholding and multiple colour space representation to improve classification of MMCC barcode

Colour 2D barcodes, such as the MMCC barcode, have been developed recently to improve the data capacity of monochrome barcodes. However, the use of colour imposes greater challenges in decoding the symbols correctly as different lighting conditions on the barcode vary the values of the colours significantly. Hence, it is desirable to have an adaptive classification of the data cells so as to adapt to various lighting conditions. In this paper, we propose a classification method that is able to adapt to different lighting conditions during the classification of the MMCC data cells in a mobile environment. To highlight the effectiveness of this method, it is compared against the classification of the data cells performed in two different colour space representations, namely RGB and YCbCr

Advances in metal additive manufacturing: A review of common processes, industrial applications, and current challenges

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. In recent years, Additive Manufacturing (AM), also called 3D printing, has been expanding into several industrial sectors due to the technology providing opportunities in terms of improved functionality, productivity, and competitiveness. While metal AM technologies have almost unlimited potential, and the range of applications has increased in recent years, industries have faced challenges in the adoption of these technologies and coping with a turbulent market. Despite the extensive work that has been completed on the properties of metal AM materials, there is still a need of a robust understanding of processes, challenges, application‐specific needs, and considerations associated with these technologies. Therefore, the goal of this study is to present a comprehen-sive review of the most common metal AM technologies, an exploration of metal AM advancements, and industrial applications for the different AM technologies across various industry sectors. This study also outlines current limitations and challenges, which prevent industries to fully benefit from the metal AM opportunities, including production volume, standards compliance, post processing, product quality, maintenance, and materials range. Overall, this paper provides a survey as the benchmark for future industrial applications and research and development projects, in order to assist industries in selecting a suitable AM technology for their application