This study proposed a harvesting system based on the Internet of Things technology and smart image recognition. Farming decisions require extensive experience; with the proposed system, crop maturity can be determined through object detection by training neural network models, and mature crops can then be harvested using robotic arms. Keras was used to construct a multilayer perceptron machine learning model and to predict multiaxial robotic arm movements and position. Following the execution of object detection on images, the pixel coordinates of the central point of the target crop in the image were used as neural network input, whereas the robotic arms were regarded as the output side. A MobileNet version 2 convolutional neural network was then used as the image feature extraction model, which was combined with a single shot multibox detector model as the posterior layer to form an object detection model. The model then performed crop detection by collecting and tagging images. Empirical evidence shows that the proposed model training had a mean average precision (mAP) of 84%, which was higher than that of other models; a mAP of 89% was observed from the arm picking results.

The Internet of Vehicle (IoV) utilizes networks to conduct message exchange and related services or application. In recent years, smart cities and IoVs have become areas of interest in the new generation Internet of Things development, especially since the development of intelligent transportation system has focused on bettering traffic conditions. This paper proposes establishing an intelligent transportation system with a network security mechanism in an IoV environment, with emphasis on the following aspects: 1) this paper integrates intelligent transportation systems in traffic signal control to aid emergency vehicles in more promptly arriving at its destination; 2) in the case of traffic incidents, this paper's approach allows regular vehicles to obtain proof of incident from pertaining authorities and learn about nearby vehicles global positioning system information, such as position and speed, and utilize their car camcorder data for proving purposes; and 3) this paper combines roadside units (RSUs) with traffic signal control and transmits important information to the certificate authority (CA) for storage. Given that RSUs are limited in computation ability and storage space, we can assess and filter the information before sending it to the CA, reducing RSUs computational burden and storage space usage. This paper satisfies IoVs network security requirements of authentication, non-repudiation, conditional anonymity, and conditional untraceability, and, as seen from experiment results, the proposed method is superior to that of other studies.

This research aims to apply an electronic nose (e-Nose) identifying TB through exhaled breath combined with machine learning methods. For the e-Nose system, participants breath normally into the e-Nose chamber. The e-Nose classified the exhaled breath into Tuberculosis Suspect (TBS) and Healthy Person (HP). The dataset was created by collecting directly after pulmonologist confirmed by using chest x-ray images. The machine learning methods were used to train and classify the exhaled breath. E-Nose models were created by using Support Vector Machine (SVM), Random Forest, and XGBoost. Based on experiments, the exhaled breath accuracy is 92 % for SVM, 88.24% for XGBoost, 94.87% for ANN, and 84.24% for the random forest.

In the production process from green beans to coffee bean packages, the defective bean removal (or in short, defect removal) is one of most labor-consuming stages, and many companies investigate the automation of this stage for minimizing human efforts. In this paper, we propose a deep-learning-based defective bean inspection scheme (DL-DBIS), together with a GAN (generative-adversarial network)-structured automated labeled data augmentation method (GALDAM) for enhancing the proposed scheme, so that the automation degree of bean removal with robotic arms can be further improved for coffee industries. The proposed scheme is aimed at providing an effective model to a deep-learning-based object detection module for accurately identifying defects among dense beans. The proposed GALDAM can be used to greatly reduce labor costs, since the data labeling is the most labor-intensive work in this sort of solutions. Our proposed scheme brings two main impacts to intelligent agriculture. First, our proposed scheme is can be easily adopted by industries as human effort in labeling coffee beans are minimized. The users can easily customize their own defective bean model without spending a great amount of time on labeling small and dense objects. Second, our scheme can inspect all classes of defective beans categorized by the SCAA (Specialty Coffee Association of America) at the same time and can be easily extended if more classes of defective beans are added. These two advantages increase the degree of automation in the coffee industry. The prototype of the proposed scheme was developed for studying integrated tests. Testing results of a case study reveal that the proposed scheme can efficiently and effectively generate models for identifying defective beans with accuracy and precision values up to $80\%$ .

In this paper, we describe an electronic nose (e-Nose) capable of classifying the aroma of alcoholic beverages. The novelty of this research is using signal processing for initial feature extraction from a sensor and then the use of deep learning to identify patterns of alcoholic beverage aromas. The sensor array was formed by nine types of metal oxide semiconductor sensors. The dataset was formed by images of standard deviations and correlation coefficients for processed signals from the e-Nose sensors. Thus, two patterns were generated. The first pattern came from a polar-chart image of the processed signals' standard deviations. The second pattern was produced by correlation coefficient converted into 3D heat-map images. The image size is 256 × 256 pixels. The convolutional architecture for fast feature embedding framework then trained GoogLeNet network using the dataset images. The training process was configured for 300 epochs and 0.0001 learning rate. The GoogLeNet network model from deep learning was compared with the AlexNet network model. The final classification was based on two patterns of prediction. The true label is used if the prediction accuracy value exceeds 70 %. The result is true only if both 3D heatmap and polar-chart prediction have true labels. The aroma detection accuracies of the GoogLeNet model are 85.0% for polar-chart and 85.416% accuracy for 3D heat-map. The aroma identification accuracy of AlexNet model are 85.0% for polarchart and 85.416% accuracy for 3D heat-map.