Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning by Abdul Razzaq, Sharaiz Shahid, Muhammad Akram, Muhammad Ashraf, Shahid Iqbal, Aamir Hussain, M. Azam Zia, Sulman Qadri, Najia Saher, Faisal Shahzad, Ali Nawaz Shah, Aziz-ur Rehman, Sven-Erik Jacobsen

“…In this research, we have developed an automatic system for stomata state identification and counting in quinoa leaf images through the transformed learning (neural network model Single Shot Detector) approach. …”
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Multistage Training and Fusion Method for Imbalanced Multimodal UAV Remote Sensing Classification by Shihao Wang, Zhengwei Xu, Yun Lin

“…In remote sensing applications, autonomous aerial vehicles (AAVs) overcome the limitations of single-sensor approaches by integrating multiple sensors and fusing cross-modal data, significantly improving target classification accuracy. …”
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Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism by Aarthi Chelladurai, D.P. Manoj Kumar, S. S. Askar, Mohamed Abouhawwash, Mohamed Abouhawwash

“…The data was gathered from the PlantVillage dataset and the pre-processing was conducted based on image resizing, color enhancement, and data augmentation. …”
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Image-based temporal profiling of autophagy-related phenotypes by Nitin Sai Beesabathuni, Neil Alvin B. Adia, Eshan Thilakaratne, Ritika Gangaraju, Priya S. Shah

“…In this study, we developed a scalable image-based temporal profiling approach to characterise ~900 morphological features at a single cell level with high temporal resolution. …”
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Laser-induced Breakdown Spectroscopy Based on Pre-classification Strategy for Quantitative Analysis of Rock Samples by Weiheng KONG, Lingwei ZENG, Yu RAO, Sha CHEN, Xu WANG, Yanting YANG, Yixiang DUAN, Qingwen FAN

“…Compared to the traditional standard curve quantitative method, using the pre-classification method can reduce the influence of different rock matrices on each other, thus reducing errors caused by the non-uniform matrix of samples.RESULTSDue to the influence of matrix effects, a single pre-processing method is not suitable for all elements in quantitative analysis. …”
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Multifrequency backscatter classification of seabed sediments using MBES: an integrated approach with ground-truth validation by Moonsoo Lim, Jeongwon Kang, Sunhee Hwang, Eunho Jung, Byung-Cheol Kum, Jongsin Kim

“…This study investigated the utility of multifrequency multibeam echosounder (MBES) backscatter data for improving seabed sediment classification compared to traditional single-frequency approaches. …”
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Evaluation of an enhanced ResNet-18 classification model for rapid On-site diagnosis in respiratory cytology by Wei Gong, Deep K. Vaishnani, Xuan-Chen Jin, Jing Zeng, Wei Chen, Huixia Huang, Yu-Qing Zhou, Khaing Wut Yi Hla, Chen Geng, Jun Ma

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Representation Learning of Multi-Spectral Earth Observation Time Series and Evaluation for Crop Type Classification by Andrea González-Ramírez, Clement Atzberger, Deni Torres-Roman, Josué López

“…Here, we propose a conceptually and computationally simple representation learning (RL) approach based on autoencoders (AEs) to generate discriminative features for crop type classification. The proposed methodology includes a set of single-layer AEs with a very limited number of neurons, each one trained with the mono-temporal spectral features of a small set of samples belonging to a class, resulting in a model capable of processing very large areas in a short computational time. …”
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Cotton Crop Classification using Optical and Microwave Remote Sensing Datasets in Google Earth Engine by B. Meerasha, M. Sagayam

“…Through experiments, we also discovered that employing time-series imagery generates substantially higher classification results than single-period images. The inclusion of shortwave infrared bands, followed by the addition of red-edge bands, can increase crop classification accuracy more than using simply traditional bands like the visible and near-infrared bands. …”
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Deep Multi-Component Neural Network Architecture by Chafik Boulealam, Hajar Filali, Jamal Riffi, Adnane Mohamed Mahraz, Hamid Tairi

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imageseg: An R package for deep learning‐based image segmentation by Jürgen Niedballa, Jan Axtner, Timm Fabian Döbert, Andrew Tilker, An Nguyen, Seth T. Wong, Christian Fiderer, Marco Heurich, Andreas Wilting

“…Abstract Convolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications, and are particularly suited for image data. Image segmentation (the classification of all pixels in images) is one such application and can, for example, be used to assess forest structural metrics. …”
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Simplifying Masked Image Modeling With Symmetric Masking and Contrastive Learning by Khanh-Binh Nguyen, Chae Jung Park

“…Despite its simplicity, SymMIM achieves state-of-the-art accuracy of 85.9% on ImageNet with ViT-Large and consistently outperforms prior methods across various downstream tasks, including image classification, semantic segmentation, object detection, and instance segmentation—all while requiring only a single-stage pre-training process.…”
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Research on Abnormal State Detection of CZ Silicon Single Crystal Based on Multimodal Fusion by Lei Jiang, Haotan Wei, Ding Liu

“…Finally, the time–frequency domain features are combined with the meniscus image features and fed into fully connected layers for multi-class classification. …”
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Multi-functional broadband diffractive neural network with a single spatial light modulator by Bolin Li, Yinfei Zhu, Jinlei Fei, Runshi Zheng, Min Gu, Jian Lin

“…It can function either as a two-layer DNN or a one-layer DNN with the other serving as an information input layer. Single- and dual-wavelength filtering and focusing, as well as spatial wavelength demultiplexing of supercontinuum, are experimentally demonstrated using the two-layer DNN, whereas the one-layer DNN is experimentally demonstrated by the classification of hand-written digits, which are input by the first layer via holographic imaging. …”
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Hand segmentation pipeline from depth map: an integrated approach of histogram threshold selection and shallow CNN classification by Zhengze Xu, Wenjun Zhang

“…We propose a new approach which is a three-stage pipeline to fast and accurate segment hand from a single depth image. Firstly, a depth frame is segmented into several regions by histogram-based thresholds selection algorithm and tracing the exterior boundaries of objects. …”
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DualTransAttNet: A Hybrid Model with a Dual Attention Mechanism for Corn Seed Classification by Fei Pan, Dawei He, Pengjun Xiang, Mengdie Hu, Daizhuang Yang, Fang Huang, Changmeng Peng

“…Hyperspectral and RGB image data were then integrated to complement one another’s information and mitigate the insufficient feature diversity caused by single-source data. …”
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A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…Traditional manual monitoring and image processing methods exhibit deficiencies in real-time performance and accuracy. …”
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Land cover classification using Land Parcel Identification System (LPIS) data and open source Eo-Learn library by Fatih Fehmi Şimşek, Süleyman Savaş Durduran

“…Instead of land cover classification from a single image, 17 different time (multi-temporal) images between 01.02.2020 and 31.11.2020 were used. …”
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Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques by Uwe Knauer, Sebastian Warnemünde, Patrick Menz, Bonito Thielert, Lauritz Klein, Katharina Holstein, Miriam Runne, Wolfgang Jarausch

“…Classification models like rRBF achieved an accuracy of 0.971 in a controlled environment with stratified data for a single variety. …”
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Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning. by Jiayi Wu, Yong-Bei Ma, Charles Congdon, Bevin Brett, Shuobing Chen, Yaofang Xu, Qi Ouyang, Youdong Mao

“…However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. …”
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