Currently, most agricultural products in developing countries are exported to many countries around the world. Therefore, the classification of these products according to different standards is necessary. In Vietnam, dragon fruit is considered as the fruit with the highest export rate. Currently, the classification of dragon fruit is carried manually, lead to low-quality classification high labor costs. Therefore, this study describes an automatic dragon fruit classifying system using non-destructive measurements, based on a convolutional neural network (CNN). This classifying system uses a combination of a model of machine learning and image processing using a convolutional neural network to identify the external features of dragon fruits; the fruits are then classified and evaluated by groups. The dragon fruit is recognized by the system, which extracts the objects combined with the signal obtained from the loadcell to calculate and determine dragon fruit in each group. The training data are collected from the dragon fruit processing system, with a dataset of images obtained from more than 1287 dragon fruits, to train the model. In this system, the classification of the processing speed and accuracy are the two most important factors. The results show that the classification system achieves high efficiency. The system is effective with existing dragon fruit types. In Vietnamese factories, the processing speed of the system increases the sorting capacity of export packing facilities to six times higher than that of the manual method, with an accuracy of more than 96%.

Nowadays, many image processing and machine learning (ML) methods are used in mango-quality classification systems. Existing algorithms perform independently to capture the relationships between features in the dataset. Nevertheless, each method has its advantages and disadvantages. As a result, this study presents an ensemble-learning framework that combines the strengths of multiple ML algorithms to make predictions. Initially, different image processing algorithms are used to extract external mango features. Next, the dataset is constructed by combining those features with weight values from sensor signals. Following that, different ML algorithms are evaluated on the dataset to determine which ones are robust. Subsequently, various ensemble-learning approaches are deployed, such as bagging, boosting, and stacking. Finally, those models are evaluated and compared, to decide which model is suited for this study’s dataset. In the experimental part, the assessment of errors demonstrates the usefulness of image processing algorithms. Furthermore, evaluation of the training models revealed that the stacking model, which integrates several methods in both the base learner and meta-learner, produced the highest results in precision, recall, F1-score, and accuracy, with values of 0.9855, 0.9901, 0.9876, and 0.9863, respectively. These experimental results confirm the robustness of the proposed methodologies for classifying mango quality.

Measuring and labeling human face landmarks are time-consuming jobs that are conducted by experts. Currently, the applications of the Convolutional Neural Network (CNN) for image segmentation and classification have made great progress. The nose is arguably one of the most attractive parts of the human face. Rhinoplasty surgery is increasingly performed in females and also in males since surgery can help to enhance patient satisfaction with the resulting perceived beautiful ratio following the neoclassical proportions. In this study, the CNN model is introduced to extract facial landmarks based on medical theories: it learns the landmarks and recognizes them based on feature extraction during training. The comparison between experiments has proved that the CNN model can detect landmarks depending on desired requirements. Anthropometric measurements are carried out by automatic measurement divided into three images with frontal, lateral, and mental views. Measurements are performed including 12 linear distances and 10 angles. The results of the study were evaluated as satisfactory with a normalized mean error (NME) of 1.05, an average error for linear measurements of 0.508 mm, and 0.498° for angle measurements. Through its results, this study proposed a low-cost automatic anthropometric measurement system with high accuracy and stability.

Facial anthropometrics are measurements of human faces and are important figures that are used in many different fields, such as cosmetic surgery, protective gear design, reconstruction, etc. Therefore, the first procedure is to extract facial landmarks, then measurements are carried out by professional devices or based on experience. The aim of this review is to provide an update and review of 3D facial measurements, facial landmarks, and nasal reconstruction literature. The novel methods to detect facial landmarks including non-deep and deep learning are also introduced in this paper. Moreover, the nose is the most attractive part of the face, so nasal reconstruction or rhinoplasty is a matter of concern, and this is a significant challenge. The documents on the use of 3D printing technology as an aid in clinical diagnosis and during rhinoplasty surgery are also surveyed. Although scientific technology development with many algorithms for facial landmarks extraction have been proposed, their application in the medical field is still scarce. Connectivity between studies in different fields is a major challenge today; it opens up opportunities for the development of technology in healthcare. This review consists of the recent literature on 3D measurements, identification of landmarks, particularly in the medical field, and finally, nasal reconstruction technology. It is a helpful reference for researchers in these fields.

An automatic system for cantaloupe flower pollination in greenhouses is proposed to meet the requirements of automatic pollination. The system consists of a mobile platform, robotic manipulator, and camera that reaches the flowers to detect and recognise their external features. The main task of the vision system is to detect the position and orientation of the flower in Cartesian coordinates, allowing the manipulator to reach the pose and perform pollination. A comprehensive method to ensure the accuracy of the pollination process is proposed that accurately determines the position and orientation of cantaloupe flowers in real environments. The vision system is used to capture images, detect the flower, and recognise its state according to its external features, such as size, colour, and shape, thereby providing appropriate nozzle access during pollination. The proposed approach begins with a segmentation method designed to precisely locate and segment the target cantaloupe flowers. Subsequently, a mathematical model is used to determine the key points that are important for establishing the growth orientation of each flower. Finally, an inverse-projection method is employed to convert the position of the flower from a two-dimensional (2D) image into a three-dimensional (3D) space, providing the necessary position for the pollination robot. The experimental process is conducted in a laboratory and proves the efficacy of the cantaloupe flower segmentation method, yielding precision, recall, and F1 scores of 87.91%, 90.76%, and 89.31%, respectively. Furthermore, the accuracy of the growth-orientation prediction method reaches approximately 86.7%. Notably, positional errors in 3D space predominantly fall within the allowable range, resulting in a successful pollination rate of up to 83.1%.

In this study, a PID-nested nonsingular terminal sliding controller is proposed to minimize the chattering phenomenon. By adding both integral and derivative errors of states into the nonsingular terminal sliding manifolds, a composite sliding manifold was created. Compared to nonsingular terminal sliding mode (NTSM) techniques, this sliding manifold can handle higher-order derivatives. The speed of the motor is controlled by a sliding control law determined through a higher-order integral, making the signal continuous, and the sliding manifold is achieved in finite time. A special full-order terminal sliding mode manifold is introduced, which allows the system to converge in finite time while being chattering-free and avoiding the singularity phenomenon of conventional and terminal sliding modes. The controller’s efficiency is demonstrated with faster convergence time and fewer errors than state-of-the-art controllers, which is demonstrated through both simulation and experiment.