An accurate image retrieval technique is required due to the rapidly increasing number of images. It is important to implement image annotation techniques that are fast, simple, and, most importantly, automatically annotate. Image annotation has recently received much attention due to the massive rise in image data volume. Focusing on the agriculture field, this study implements automatic image annotation, namely, a repetitive annotation task technique, to classify the ripeness of oil palm fruit and recognize a variety of fruits. This approach assists farmers to enhance the classification of fruit methods and increase their production. This study proposes simple and effective models using a deep learning approach with You Only Look Once (YOLO) versions. The models were developed through transfer learning where the dataset was trained with 100 images of oil fruit palm and 400 images of a variety of fruit in RGB images. Model performance and accuracy of automatically annotating the images with 3500 fruits were examined. The results show that the annotation technique successfully annotated a large number of images accurately. The mAP result achieved for oil palm fruit was 98.7% and the variety of fruit was 99.5%.

This paper presents a linear and nonlinear stochastic distribution for the interactions in multi-agent systems (MAS). The interactions are considered for the agents to reach a consensus using hetero-homogeneous transition stochastic matrices. The states of the agents are presented as variables sharing information in the MAS dynamically. The paper studies the interaction among agents for the attainment of consensus by limit behavior from their initial states' trajectories. The paper provides a linear distribution of DeGroot model compared with a nonlinear distribution of change stochastic quadratic operators (CSQOs), doubly stochastic quadratic operators (DSQOs) and extreme doubly Reach a nonlinear consensus for MAS via doubly stochastic quadratic operators Cited 3 times www.tandf.co.uk/journals/titles/00207179.asp

Radio frequency identification (RFID) is a rapidly developing technology, and RFID sensors have become important components in many common technology applications. The passive ultra-high frequency (UHF) tags used in RFID sensors have a higher data transfer rate and longer read range and usually come in unique small and portable application designs. However, these tags suffer from significant frequency interference when mounted on metallic materials or placed near liquid surfaces. This paper presents the recent advancements made in passive UHF-RFID tag designs proposed to resolve the interference problems. We focus on those designs that are intended to improve antenna read range as well as scalability designs for miniaturized applications.

In this paper, we consider nonlinear models of DeGroot, quadratic stochastic operators (QSO) and doubly stochastic quadratic operators (DSQO) with fractional degree for consensus problem in multi -agent systems (MAS). By the limit behaviour of nonlinear approach, we discuss the convergence of the solutions of the models considered. The findings from the results of the carried out investigation demonstrates an efficient approach to convergence for consensus problem in MAS. The main advantages of the proposed work are i) fast convergence to consensus ii) flexible and low complexity in computation iii) ability to achieve optimal consensus . The study is built on fractional representation of [Formula presented] where n → ∞. Further, the simulation results on the related protocols are also presented.