Yelp has been one of the most popular local service search engine in US since 2004. It is powered by crowd-sourced text reviews and photo reviews. Restaurant customers and business owners upload photo images to Yelp, including reviewing or advertising either food, drinks, or inside and outside decorations. It is obviously not so effective that labels for food photos rely on human editors, which is an issue should be addressed by innovative machine learning approaches. In this paper, we present a simple but effective approach which can identify up to ten kinds of food via raw photos from the challenge dataset. We use 1) image pre-processing techniques, including filtering and image augmentation, 2) feature extraction via convolutional neural networks (CNN), and 3) three ways of classification algorithms. Then, we illustrate the classification accuracy by tuning parameters for augmentations, CNN, and classification. Our experimental results show this simple but effective approach to identify up to 10 food types from images.
FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network
School of Information Science and Engineering, Shandong Normal University（山东师范大学信息科学与工程学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
GCAM: Gaussian and causal-attention model of food fine-grained recognition
School of Computer and Information, Hefei University of Technology（合肥工业大学计算机与信息学院）；School of Food Science and Engineering, Northern University for Nationalities（北方民族大学食品科学与工程学院）；School of Electrical Engineering, Xinjiang University（新疆大学电气工程学院）；School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）
MFP3D: Monocular Food Portion Estimation Leveraging 3D Point Clouds
Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, USA（电气与计算机工程学院，普渡大学，西拉法叶，美国）；College of Artificial Intelligence, Anhui University, Hefei, China（人工智能学院，安徽大学，合肥，中国）
Mam-App: A Novel Parameter-Efficient Mamba Model for Apple Leaf Disease Classification
Computer Science and Engineering, Begum Rokeya University（计算机科学与工程，贝 Gum 罗基亚大学）；Computer Science, Kumamoto University（计算机科学，熊本大学）；Information and Communication Technologies, Asian Institute of Technology（信息与通信技术，亚洲理工学院）；Computer Science, Texas State University（计算机科学，德克萨斯州立大学）
ViTaL: An Advanced Framework for Automated Plant Disease Identification in Leaf Images Using Vision Transformers and Linear Projection For Feature Reduction
A Systematic Review of Machine Learning Techniques for Cattle Identification: Datasets, Methods and Future Directions
Charles Sturt University（查尔斯特大学）；Food Agility CRC Ltd（Food Agility合作研究中心有限公司）；TerraCipher Pty. Ltd.（TerraCipher私人有限公司）
生物安保和食品安全要求的提高可能会增加对供应链中牲畜有效追踪和识别系统的需求。机器学习和计算机视觉的先进技术已应用于精准畜牧管理，包括重大疾病检测、疫苗接种、生产管理、跟踪和健康监测。本文提供了一个系统的文献综述（SLR）基于视觉的牛识别。更具体地说，这个SLR是使用机器学习（ML）和深度学习（DL）识别和分析与牛识别相关的研究。对于牛的检测和牛的识别这两个主要应用，所有基于ML的论文都只解决牛的识别问题。然而，在基于DL的论文中研究了检测和识别问题。根据我们的调查报告，用于牛识别的最常用的ML模型是支持向量机（SVM），k-最近邻（KNN）和人工神经网络（ANN）。卷积神经网络（CNN）、残差网络（ResNet）、Inception、You Only Look Once（YOLO）和Faster R-CNN是所选论文中流行的DL模型。在这些纸张中，最显著的特征是牛的口印和皮毛图案。局部二值模式（LBP），加速鲁棒特征（SURF），尺度不变特征变换（SIFT），以及Inception或CNN被认为是最常用的特征提取方法。
ELMF4EggQ: Ensemble Learning with Multimodal Feature Fusion for Non-Destructive Egg Quality Assessment
organization= Department of Computer Science；Engineering, Bangladesh Army University of Science；Engineering, Rajshahi University of Engineering \& Technology , state= Rajshahi , postcode= 6204 , country= Bangladesh；organization= School of Computing, Mathematics；Engineering, Charles Sturt University , city= Bathurst , state= NSW , postcode= 2795 , country= Australia；organization= Gulbali Institute for Agriculture, Water；Environment, Charles Sturt University , city= Wagga Wagga , state= NSW , postcode= 2678 , country= Australia；organization= Department of Animal Production；Management, Sher-e-Bangla Agricultural University , city= Dhaka , country= Bangladesh
