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Estimating the calorie content of fruits is critical for weight management and maintaining overall health as well as aiding individuals in making informed dietary choices. Accurate knowledge of fruit calorie content assists in crafting personalized nutrition plans and preventing obesity and associated health issues. In this paper, we investigate the application of deep learning models for estimating the calorie content in fruits from digital images, aiming to provide a more efficient and accurate method for nutritional analysis. We create a dataset comprising images of various fruits and employ random data augmentation techniques during training to enhance model robustness. We utilize the RT-DETR model integrated into the ultralytics framework for implementation and conduct comparative experiments with YOLOv10 on the dataset. Our results show that the RT-DETR model achieved a precision rate of 99.01% and mAP50-95 of 94.45% in fruit detection from digital images, outperforming YOLOv10 in terms of F1- Confidence Curves, P-R curves, precision, and mAP. Conclusively, in this paper, we utilize a transformer architecture to detect fruits and estimate their calorie and nutritional content. The results of the experiments provide a technical reference for more accurately monitoring an individual's dietary intake by estimating the calorie content of fruits. [ABSTRACT FROM AUTHOR]

In the digital age, where the Internet of Things (IoT) permeates every facet of our lives, the safeguarding of data privacy, especially video data, emerges as a paramount concern. The ubiquity of IoT devices, capable of capturing and disseminating vast quantities of video data, introduces unprecedented challenges in ensuring the privacy and security of such information. This article explores the crucial intersection of video data privacy and blockchain technology within IoT networks. It aims to uncover and articulate the unique challenges video data encounter in the IoT ecosystem, such as susceptibility to unauthorized access and the difficulty in ensuring data integrity and confidentiality. By conducting a thorough literature review, this study not only illuminates the intricate privacy challenges inherent in IoT environments but also showcases the immutable, decentralized nature of blockchain as a potent solution. We systematically explore how blockchain-based methods can be pragmatically implemented to fortify video data privacy, scrutinizing the efficacy of these approaches in the IoT context. Through critical assessment, the paper delineates the strengths and limitations of video blockchain solutions, underscoring the transformative potential of blockchain technology as a cornerstone for enhancing data privacy in IoT networks. Conclusively, this work advocates for blockchain as an indispensable tool in the advancement of data privacy measures for video content, thereby reinforcing trust and security in the increasingly connected fabric of our digital world. As IoT applications burgeon, the fusion of blockchain technology with IoT infrastructures promises a robust framework for protecting sensitive video data, heralding a future of enhanced trust and security in our interconnected ecosystem. [ABSTRACT FROM AUTHOR]

The density of new shoots on pine trees is an important indicator of their growth and photosynthetic capacity. However, traditional methods to monitor new shoot density rely on manual and destructive measurements, which are labor-intensive and have led to fewer studies on new shoot density. Therefore, in this study, we present user-friendly software called CountShoots, which extracts new shoot density in an easy and convenient way using unmanned aerial vehicles based on the YOLOX and Slash Pine Shoot Counting Network (SPSC-net) models. This software mainly consists of 2 steps. Firstly, we deployed a modified YOLOX model to identify the tree species and location from complex RGB background images, which yielded a high recognition accuracy of 99.15% and 95.47%. These results showed that our model produced higher detection accuracy compared to YOLOv5, Efficientnet, and Faster-RCNN models. Secondly, we constructed an SPSC-net. This methodology is based on the CCTrans network, which outperformed DM-Count, CSR-net, and MCNN models, with the lowest mean squared error and mean absolute error results among other models (i.e., 2.18 and 1.47, respectively). To our best knowledge, our work is the first research contribution to identify tree crowns and count new shoots automatically in slash pine. Our research outcome provides a highly efficient and rapid user-interactive pine tree new shoot detection and counting system for tree breeding and genetic use purposes., (Copyright © 2023 Xia Hao et al.)