Automated plant species identification system could help botanists and layman in identifying plant species rapidly. Deep learning is robust for feature extraction as it is superior in providing deeper information of images. In this research, a new CNN-based method named D-Leaf was proposed. The leaf images were pre-processed and the features were extracted by using three different Convolutional Neural Network (CNN) models namely pre-trained AlexNet, fine-tuned AlexNet and D-Leaf. These features were then classified by using five machine learning techniques, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), k-Nearest-Neighbour (k-NN), Naïve-Bayes (NB) and CNN. A conventional morphometric method computed the morphological measurements based on the Sobel segmented veins was employed for benchmarking purposes. The D-Leaf model achieved a comparable testing accuracy of 94.88% as compared to AlexNet (93.26%) and fine-tuned AlexNet (95.54%) models. In addition, CNN models performed better than the traditional morphometric measurements (66.55%). The features extracted from the CNN are found to be fitted well with the ANN classifier. D-Leaf can be an effective automated system for plant species identification as shown by the experimental results.

Flexible manufacturing system (FMS) enhances the firm’s flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs’ battery charge. Assessment of the numerical examples’ scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

This paper presents a fuzzy extreme learning machine (F-ELM) that embeds fuzzy membership functions and rules into the hidden layer of extreme learning machine (ELM). Similar to the concept of ELM that employed the random initialization technique, three parameters of F-ELM are randomly assigned. They are the standard deviation of the membership functions, matrix-C (rule-combination matrix), and matrix-D [don't care (DC) matrix]. Fuzzy if-then rules are formulated by the rule-combination Matrix of F-ELM, and a DC approach is adopted to minimize the number of input attributes in the rules. Furthermore, F-ELM utilizes the output weights of the ELM to form the target class and confidence factor for each of the rules. This is to indicate that the corresponding consequent parameters are determined analytically. The operations of F-ELM are equivalent to a fuzzy inference system. Several benchmark data sets and a real world fault detection and diagnosis problem have been used to empirically evaluate the efficacy of the proposed F-ELM in handling pattern classification tasks. The results show that the accuracy rates of F-ELM are comparable (if not superior) to ELM with distinctive ability of providing explicit knowledge in the form of interpretable rule base.

In this article, the development of an augmented reality–based robotic work cell is presented, consisting of a virtual robot arm, conveyor belt, pallet and computer numerical control machine that simulates an actual manufacturing plant environment. The kinematics of the robot arm is realized using Denavit–Hartenberg’s theorem, which enables complete manipulation of the end-effector in three-dimensional space when interacting with other virtual machines. Collision detection is implemented in two areas, namely, modifiable marker–based detection for the robot arm, which detects nearby obstacles as well as integration with object manipulation to pick and place a virtual object around the environment. In addition, an augmented heads-up display overlay displays live information of the current system. The case studies suggest that the proposed system can simulate a collision-free operation while displaying the coordinates of the virtual object, current tool equipped and speed of the conveyor belt, with a percentage error of less than 5%.

The high-mix, low-volume (HMLV) industry has seen growth in the need for product customisation with research to increase manufacturers’ flexibility for the variation in market demands. This paper reviews 152 documents from 2000 to October 2022, discussing work related to HMLV production. From an industrial perspective, this paper analyses the industries with HMLV, revealing production sectors and research areas, categorising the developed work, type of validation, and applications. The results show that most work is not industry-specific, with production planning as the central aspect of the research. While other parts of the production processes and value chain received less attention, the semiconductor and electronics industries are the two most researched with substantial validation, leaving gaps in other industries. Earlier work primarily focused on the theoretical development of production planning; however, the development of Industry 4.0 technologies advocates decision support systems for reactive production planning. This period sees the rise in robotics and automation, with improved robotics capability and human—robot collaboration. Assembly assistance systems were developed for manual production to aid operators in managing the variety of information. This paper serves as a reference for the HMLV manufacturing industry in a structured manner while identifying potential for future research in this field.