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A Subpixel Target Detection Approach to Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing 2017年第9期55卷 5093-5114页

作者： Xue, Bai Yu, Chunyan Wang, Yulei Song, Meiping Li, Sen Wang, Lin Chen, Hsian-Min Chang, Chein-I Center for Hyperspectral Imaging in Remote Sensing Information and Technology College Dalian Maritime University Dalian China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States Key Laboratory of Spectral Imaging Technology Chinese Academy of Sciences Xi'an China State Key Laboratory of Integrated Services Networks Xi'an China School of Physics and Optoelectronic Engineering Xidian University Xi'an China Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Computer Science and Information Management Providence University Taichung02912 Taiwan

Hyperspectral image classification faces various levels of difficulty due to the use of different types of hyperspectral image data. Recently, spectral-spatial approaches have been developed by jointly taking care of spectral and spatial information. This paper presents a completely different approach from a subpixel target detection view point. It implements four stage processes, a preprocessing stage, which uses band selection (BS) and nonlinear band expansion, referred to as BS-then-nonlinear expansion (BSNE), a detection stage, which implements constrained energy minimization (CEM) to produce subpixel target maps, and an iterative stage, which develops an iterative CEM (ICEM) by applying Gaussian filters to capture spatial information, and then feeding the Gaussian-filtered CEM-detection maps back to BSNE band images to reprocess CEM in an iterative manner. Finally, in the last stage Otsu's method is applied to converting ICEM-detected real-valued maps to discrete values for classification. The entire process is called BSNE-ICEM. Experimental results demonstrate BSNE-ICEM, which has advantages over support vector machine-based approaches in many aspects, such as easy implementation, fewer parameters to be used, and better false classification and precision rates. © 1980-2012 IEEE.

关键词： Support vector machines

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Comments on "Design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization"

arXiv

引用

arXiv 2018年

作者： Khan, Shujaat Wahab, Abdul Naseem, Imran Moinuddin, Muhammad Bio-imaging and Signal Processing Laboratory Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Daejeon Yuseong-gu34141 Korea Republic of Department of Mathematics University of Education Attock Campus43600 Pakistan School of Electrical Electronic and Computer Engineering University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia College of Engineering Karachi Institute of Economics and Technology Korangi Creek Karachi75190 Pakistan King Abdulaziz University Jeddah Saudi Arabia Electrical and Computer Engineering Department King Abdulaziz University Jeddah Saudi Arabia

The purpose of this note is to highlight some critical flaws in recently proposed fractional-order variants of complex least mean square (CLMS) and normalized least mean square (NLMS) algorithms in "Design of Fractional-order Variants of Complex LMS and Normalized LMS Algorithms for Adaptive Channel Equalization" [Non-linear Dyn. 88(2), 839-858 (2017)]. It is substantiated that these algorithms do not always converge whereas they have no advantage over the CLMS and NLMS algorithms whenever they converge. Our claims are based on analytical reasoning supported by simulations. Copyright © 2018, The Authors. All rights reserved.

关键词： Equalizers

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Statistical study of electron density turbulence and ion-cyclotron waves in the inner heliosphere: Solar orbiter observations

arXiv

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arXiv 2021年

作者： Carbone, F. Sorriso-Valvo, Luca Khotyaintsev, Yu.V. Steinvall, K. Vecchio, A. Telloni, D. Yordanova, E. Graham, D.B. Edberg, N.J.T. Eriksson, A.I. Johansson, E.P.G. Vásconez, C.L. Maksimovic, M. Bruno, R. D’Amicis, R. Bale, S.D. Chust, T. Krasnoselskikh, V. Kretzschmar, M. Lorfèvre, E. Plettemeier, D. Souček, J. Steller, M. Štverák, Š. Trávníček, P. Vaivads, A. Horbury, T.S. O’Brien, H. Angelini, V. Evans, V. National Research Council Institute of Atmospheric Pollution Research C/o University of Calabria Rende87036 Italy Ångström Laboratory Lägerhyddsvägen 1 Uppsala75121 Sweden CNR Istituto per la Scienza e Tecnologia dei Plasmi via Amendola 122/D Bari70126 Italy LESIA Observatoire de Paris Université PSL CNRS Sorbonne Université Univ. Paris Diderot Sorbonne Paris Cité 5 place Jules Janssen Meudon92195 France Research Institute for Mathematics Astrophysics and Particle Physics Radboud University Nijmegen Netherlands National Institute for Astrophysics - Astrophysical Observatory of Torino Italy Departamento de Física Escuela Politécnica Nacional Ladrón de Guevara E11-253 Quito170525 Ecuador Via Fosso del Cavaliere 100 Rome00133 Italy Space Sciences Laboratory University of California BerkeleyCA United States Physics Department University of California BerkeleyCA United States LPP CNRS Ecole Polytechnique Sorbonne Université Observatoire de Paris Université Paris-Saclay Palaiseau Paris France LPC2E CNRS 3A Avenue de la Recherche Scientifique Orléans France Université d’Orléans Orléans France CNES 18 Avenue Edouard Belin Toulouse31400 France Technische Universität Dresden Würzburger Str. 35 DresdenD-01187 Germany Institute of Atmospheric Physics of the Czech Academy of Sciences Prague Czech Republic Space Research Institute Austrian Academy of Sciences Graz Austria Astronomical Institute of the Czech Academy of Sciences Prague Czech Republic Department of Space and Plasma Physics School of Electrical Engineering and Computer Science Royal Institute of Technology Stockholm Sweden Space and Atmospheric Physics The Blackett Laboratory Imperial College of London LondonSW7 2AZ United Kingdom

Context. The recently released spacecraft potential measured by the RPW instrument on-board Solar Orbiter has been used to estimate the solar wind electron density in the inner heliosphere. Aims. Solar-wind electron density measured during June 2020 has been analysed to obtain a thorough characterization of the turbulence and intermittency properties of the fluctuations. Magnetic field data have been used to describe the presence of ion-scale waves. Methods. Selected intervals have been extracted to study and quantify the properties of turbulence. The Empirical Mode Decomposition has been used to obtain the generalized marginal Hilbert spectrum, equivalent to the structure functions analysis, and additionally reducing issues typical of non-stationary, short time series. The presence of waves was quantitatively determined introducing a parameter describing the time-dependent, frequency-filtered wave power. Results. A well defined inertial range with power-law scaling has been found almost everywhere. However, the Kolmogorov scaling and the typical intermittency effects are only present in part of the samples. Other intervals have shallower spectra and more irregular intermittency, not described by models of turbulence. These are observed predominantly during intervals of enhanced ion frequency wave activity. Comparisons with compressible magnetic field intermittency (from the MAG instrument) and with an estimate of the solar wind velocity (using electric and magnetic field) are also provided to give general context and help determine the cause for the anomalous fluctuations. © 2021, CC BY.

关键词： Electrons

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Effects of RapidEye Imagery's Red-edge Band and Vegetation Indices on Land Cover Classification in an Arid Region

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Chinese Geographical Science 2017年第5期27卷 827-835页

作者： LI Xianju CHEN Gang LIU Jingyi CHEN Weitao CHENG Xinwen LIAO Yiwei College of Computer Science and Hubei Key Laboratory of Intelligent Geo-Information Processing China University of GeosciencesWuhan 430074China College of Marine Science and Technology China University of GeosciencesWuhan 430074China Laboratory of Geographic Information and Spatial Analysis Department of Geography and PlanningQueen's UniversityKingston ON K7L3N6Canada Faculty of Information Engineering China University of GeosciencesWuhan 430074China School of Electrical and Electronic Engineering Huazhong University of Science and TechnologyWuhan 430074China

Land cover classification(LCC) in arid regions is of great significance to the assessment, prediction, and management of land desertification. Some studies have shown that the red-edge band of RapidE ye images was effective for vegetation identification and could improve LCC accuracy. However, there has been no investigation of the effects of RapidE ye images' red-edge band and vegetation indices on LCC in arid regions where there are spectrally similar land covers mixed with very high or low vegetation coverage information and bare land. This study focused on a typical inland arid desert region located in Dunhuang Basin of northwestern China. First, five feature sets including or excluding the red-edge band and vegetation indices were constructed. Then, a land cover classification system involving plant communities was developed. Finally, random forest algorithm-based models with different feature sets were utilized for LCC. The conclusions drawn were as follows: 1) the red-edge band showed slight contribution to LCC accuracy; 2) vegetation indices had a significant positive effect on LCC; 3) simultaneous addition of the red-edge band and vegetation indices achieved a significant overall accuracy improvement(3.46% from 86.67%). In general, vegetation indices had larger effect than the red-edge band, and simultaneous addition of them significantly increased the accuracy of LCC in arid regions.

关键词： arid region land cover classification RapidEye red-edge band vegetation indices random forest Dunhuang Basin

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Multilinear class-specific discriminant analysis

arXiv

引用

arXiv 2017年

作者： Tran, Dat Thanh Gabbouj, Moncef Iosifidis, Alexandros Laboratory of Signal Processing Tampere University of Technology Tampere Finland Department of Engineering Electrical and Computer Engineering Aarhus University

There has been a great effort to transfer linear discriminant techniques that operate on vector data to high-order data, generally referred to as Multilinear Discriminant Analysis (MDA) techniques. Many existing works focus on maximizing the inter-class variances to intra-class variances defined on tensor data representations. However, there has not been any attempt to employ class-specific discrimination criteria for the tensor data. In this paper, we propose a multilinear subspace learning technique suitable for applications requiring class-specific tensor models. The method maximizes the discrimination of each individual class in the feature space while retains the spatial structure of the input. We evaluate the efficiency of the proposed method on two problems, i.e. facial image analysis and stock price prediction based on limit order book data. Copyright © 2017, The Authors. All rights reserved.

关键词： Tensors

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Efficient and Robust Machine Learning for Real-World Systems

arXiv

引用

arXiv 2018年

作者： Pernkopf, Franz Roth, Wolfgang Zöhrer, Matthias Pfeifenberger, Lukas Schindler, Günther Fröning, Holger Tschiatschek, Sebastian Peharz, Robert Mattina, Matthew Ghahramani, Zoubin Department of Electrical Engineering Laboratory of Signal Processing and Speech Communication Graz University of Technology Austria Institute of Computer Engineering Ruperts Karls University Heidelberg Germany Microsoft Research Cambridge United Kingdom Machine Learning Group Department of Engineering University of Cambridge United Kingdom Machine Learning Group Department of Engineering University of Cambridge United Kingdom Uber AI Labs CA United States Arm Research Arm Ltd. Cambridge United Kingdom

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource efficient approaches. These approaches require a carefully chosen tradeoff between performance and resource consumption in terms of computation and energy. On top of this, it is crucial to treat uncertainty in a consistent manner in all but the simplest applications of machine learning systems. In particular, a desideratum for any real-world system is to be robust in the presence of outliers and corrupted data, as well as being "aware" of its limits, i.e. the system should maintain and provide an uncertainty estimate over its own predictions. These complex demands are among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology into every day’s applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. First we provide a comprehensive review of resource-efficiency in deep neural networks with focus on techniques for model size reduction, compression and reduced precision. These techniques can be applied during training or as post-processing and are widely used to reduce both computational complexity and memory footprint. As most (practical) neural networks are limited in their ways to treat uncertainty, we contrast them with probabilistic graphical models, which readily serve these desiderata by means of probabilistic inference. In that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Secrecy performance analysis of SIMO underlay cognitive radio systems with outdated CSI

引用

IET Communications 2017年第12期11卷 1961-1969页

作者： Lei, Hongjiang Zhang, Jianming Park, Ki-Hong Ansari, Imran Shafique Pan, Gaofeng Alouini, Mohamed-Slim Chongqing Key Lab of Mobile Communications Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Electrical and Computer Engineering (ECEN) Texas A and M University at Qatar (TAMUQ) Education City Doha Qatar Chongqing Key Laboratory of Non-linear Circuits and Intelligent Information Processing Southwest University Chongqing 400715 China

This study investigates the secrecy outage performance of a single-input multiple-output underlay cognitive radio network (CRN) with outdated channel state information (CSI). The confidential messages are transmitted from transmitter to the destination, while a multi-antenna eavesdropper exists. The maximal ratio combining and selection combining schemes are utilised at the receivers to improve the quality of the received signal-to-noise ratio. The exact and asymptotic closed-form expressions of secrecy outage probability are derived, and simulation results are provided to verify the authors' proposed analytical results. The results reveal that imperfect CSI of main channels deteriorates the secrecy outage performance while that of eavesdropping and interfering channels has contrary effect, and only a unity diversity order can be obtained in underlay CRNs with imperfect CSI. © The Institution of engineering and technology.

关键词： single-input multiple-output underlay cognitive radio network telecommunication security interfering channels probability receivers diversity reception outdated CSI exact closed-form expressions secrecy performance analysis secrecy outage probability maximal ratio combining multiantenna eavesdropper SIMO underlay cognitive radio systems antenna arrays asymptotic closed-form expressions Antenna arrays transmitter outdated channel state information Radio links and equipment cognitive radio radio networks received signal-to-noise ratio quality selection combining schemes Other topics in statistics

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GRID: A Student Project to Monitor the Transient Gamma-Ray Sky in the Multi-Messenger Astronomy Era

arXiv

引用

arXiv 2019年

作者： Wen, Jiaxing Long, Xiangyun Zheng, Xutao An, Yu Cai, Zhengyang Cang, Jirong Che, Yuepeng Chen, Changyu Chen, Liangjun Chen, Qianjun Chen, Ziyun Cheng, Yingjie Deng, Litao Deng, Wei Ding, Wenqing Du, Hangci Duan, Lian Gan, Quan Gao, Tai Gao, Zhiying Han, Wenbin Han, Yiying He, Xinbo He, Xinhao Hou, Long Hu, Fan Hu, Junling Huang, Bo Huang, Dongyang Huang, Xuefeng Jia, Shihai Jiang, Yuchen Jin, Yifei Li, Ke Li, Siyao Li, Yurong Liang, Jianwei Liang, Yuanyuan Lin, Wei Liu, Chang Liu, Gang Liu, Mengyuan Liu, Rui Liu, Tianyu Liu, Wanqiang Lu, Di'an Lu, Peiyibin Lu, Zhiyong Luo, Xiyu Ma, Sizheng Ma, Yuanhang Mao, Xiaoqing Mo, Yanshan Nie, Qiyuan Qu, Shuiyin Shan, Xiaolong Shi, Gengyuan Song, Weiming Sun, Zhigang Tan, Xuelin Tang, Songsong Tao, Mingrui Wang, Boqin Wang, Yue Wang, Zhiang Wu, Qiaoya Wu, Xuanyi Xia, Yuehan Xiao, Hengyuan Xie, Wenjin Xu, Dacheng Xu, Rui Xu, Weili Yan, Longbiao Yan, Shengyu Yang, Dongxin Yang, Hang Yang, Haoguang Yang, Yi-Si Yang, Yifan Yao, Lei Yu, Huan Yu, Yangyi Zhang, Aiqiang Zhang, Bingtao Zhang, Lixuan Zhang, Maoxing Zhang, Shen Zhang, Tianliang Zhang, Yuchong Zhao, Qianru Zhao, Ruining Zheng, Shiyu Zhou, Xiaolong Zhu, Runyu Zou, Yu An, Peng Cai, Yifu Chen, Hongbing Dai, Zigao Fan, Yizhong Feng, Changqing Feng, Hua Gao, He Huang, Liang Kang, Mingming Li, Lixin Li, Zhuo Liang, Enwei Lin, Lin Lin, Qianqian Liu, Congzhan Liu, Hongbang Liu, Xuewen Liu, Yinong Lu, Xiang Mao, Shude Shen, Rongfeng Shu, Jing Su, Meng Sun, Hui Tam, Pak-Hin Tang, Chi-Pui Tian, Yang Wang, Fayin Wang, Jianjun Wang, Wei Wang, Zhonghai Wu, Jianfeng Wu, Xuefeng Xiong, Shaolin Xu, Can Yu, Jiandong Yu, Wenfei Yu, Yunwei Zeng, Ming Zeng, Zhi Zhang, Bin-Bin Zhang, Bing Zhao, Zongqing Zhou, Rong Zhu, Zonghong Department of Engineering Physics Center for Astrophysics Tsinghua University Beijing100084 China Science and Technology on Plasma Physics Laboratory Laser Fusion Research Center Chinese Academy of Engineering Physics Mianyang621900 China Department of Physics Center for Astrophysics Tsinghua University Beijing100084 China Guangxi Key Laboratory for Relativistic Astrophysics Department of Physics Guangxi University NanningGuangxi530004 China School of Electronic and Information Engineering Ningbo University of Technology NingboZhejiang315211 China Department of Instruments Science & Engineering Shanghai Jiaotong University Shanghai200240 · China School of Materials Science & Chemical Engineering Ningbo University Ningbo315211 China School of Astronomy and Space Science Nanjing University NanjingJiangsu210093 China School of Aerospace Engineering Tsinghua University Beijing100084 China College of Physical Science and Technology Sichuan University Chengdu610065 China School of Physics and Astronomy Sun Yat-Sen University Zhuhai519082 China Department of Astronomy Xiamen University XiamenFujian361005 China School of Mathematics and Physics University of Science and Technology Beijing Beijing100083 China Department of Foreign Languages and Literatures Tsinghua University Beijing100084 China Department of Electrical Engineering Tsinghua University Beijing100084 China School of Software Tsinghua University Beijing100084 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Department of Modern Physics School of Physical Sciences University of Science and Technology of China HefeiAnhui230026 China Department of Astronomy Beijing Normal University Beijing100875 China Department of Astronomy School of Physical Sciences University of Science and Technology of China HefeiAnhui230026 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210008 China Department of Astrono

The Gamma-Ray Integrated Detectors (GRID) is a space mission concept dedicated to monitoring the transient gamma-ray sky in the energy range from 10 keV to 2 MeV using scintillation detectors onboard CubeSats in low Earth orbits. The primary targets of GRID are the gamma-ray bursts (GRBs) in the local universe. The scientific goal of GRID is, in synergy with ground-based gravitational wave (GW) detectors such as LIGO and VIRGO, to accumulate a sample of GRBs associated with the merger of two compact stars and study jets and related physics of those objects. It also involves observing and studying other gamma-ray transients such as long GRBs, soft gamma-ray repeaters, terrestrial gamma-ray flashes, and solar flares. With multiple CubeSats in various orbits, GRID is unaffected by the Earth occultation and serves as a full-time and all-sky monitor. Assuming a horizon of 200 Mpc for ground-based GW detectors, we expect to see a few associated GW-GRB events per year. With about 10 CubeSats in operation, GRID is capable of localizing a faint GRB like 170817A with a 90% error radius of about 10 degrees, through triangulation and flux modulation. GRID is proposed and developed by students, with considerable contribution from undergraduate students, and will remain operated as a student project in the future. The current GRID collaboration involves more than 20 institutes and keeps growing. On August 29th, the first GRID detector onboard a CubeSat was launched into a Sun-synchronous orbit and is currently under test. Copyright © 2019, The Authors. All rights reserved.

关键词： Gravity waves

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Improving efficiency in convolutional neural network with multilinear filters

arXiv

引用

arXiv 2017年

作者： Tran, Dat Thanh Iosifidis, Alexandros Gabbouj, Moncef Laboratory of Signal Processing Tampere University of Technology Tampere Finland Department of Engineering Electrical and Computer Engineering Aarhus University Aarhus Denmark

The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices. However, current deep net architectures are heavy with millions of parameters and require billions of floating point operations. Several works have been developed to compress a pre-trained deep network to reduce memory footprint and, possibly, computation. Instead of compressing a pre-trained network, in this work, we propose a generic neural network layer structure employing multilinear projection as the primary feature extractor. The proposed architecture requires several times less memory as compared to the traditional Convolutional Neural Networks (CNN), while inherits the similar design principles of a CNN. In addition, the proposed architecture is equipped with two computation schemes that enable computation reduction or scalability. Experimental results show the effectiveness of our compact projection that outperforms traditional CNN, while requiring far fewer parameters. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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2025 photonics for agrifood roadmap: towards a sustainable and healthier planet

Journal of Physics: Photonics

引用

Journal of Physics: Photonics 2025年第3期7卷 032501-032501页

作者： Lien Smeesters Francesca Venturini Stefan Paulus Anne-Katrin Mahlein David Perpetuini Daniela Cardone Arcangelo Merla Benjamin Gac Stephane Perrin Denis Trégoat Luiz Poffo Antoine Fournier Giuseppe Bonifazi Silvia Serranti Wenzhi Liao Noureddin Osseiran Mathias Vanwolleghem Romain Peretti Aparajita Bandyopadhyay Karl Bertling Krishnan Parameswaran Aleksandar D Rákic Amartya Sengupta Sune Svanberg Małgorzata Kujawinska Daniel Cozzolino Dolores Pérez Marín José A Entrenas de León Irina Torres Rodríguez Marietta Fodor Eszter Benes Anna Matkovits Zsuzsa Jókai Simona M Cristescu Amir Khodabakhsh Khalil Jahromi Mohammadreza Nematollahi Frans H Harren Eugene Rokx Ewa Sikorska Kay Sowoidnich Martin Maiwald Bernd Sumpf Matthieu Roussey Kai-Erik Peiponen Antoni Femenias Polina Fomina Vjekoslav Kokoric Robert Stach Boris Mizaikoff Maria Konstantaki Stavros Pissadakis Umberto Michelucci Sławomir Paśko Anna Pakuła Richard A Crocombe Ellen V Miseo Andreas C Liapis Faisal Ahmed Zhipei Sun Chrysoula Chandrinou Marianneza Chatzipetrou Ioanna Zergioti Ulrich Trog Anja Haase Martin Smolka Lex Oosterveld Chris Van Hoof André Müller Michael Hlavatsch Diellza Bajrami Ernesto Olvera-Gonzalez Nivia Escalante-Garcia Arturo B Soro Koenraad Van Hoorde Brijesh K Tiwari Thomas Westerhoff Michael Kneissl Anna Grazia Mignani Department of Applied Physics and Photonics Vrije Universiteit Brussel and Flanders Make Brussels Photonics (B-PHOT) Pleinlaan 2 B-1050 Brussels Belgium School of Engineering ZHAW Zurich University of Applied Sciences 8401 Winterthur Switzerland TOELT LLC Research and Development 8600 Duebendorf Switzerland Institute of Sugar Beet Research Holtenser Landstr. 77 37079 Göttingen Germany Department of Engineering and Geology University ‘D’Annunzio’ of Chieti-Pescara Pescara 65127 Italy Arvalis 45 Voie Romaine 41240 Beauce la Romaine France Université de Rennes CNRS Institut FOTON—UMR 6082 F-22305 Lannion France Photonics Bretagne 4 Rue Louis de Broglie 22300 Lannion France University of Limoges XLIM UMR CNRS 7252 Limoges France Department of Chemical Engineering Materials and Environment Sapienza—University of Rome via Eudossiana 18 00184 Rome Italy Flanders Make 8500 Kortrijk Belgium Department of Telecommunications and Information Processing Ghent University 9000 Ghent Belgium Institut d’Electronique de Microélectronique et de Nanotechnologie (IEMN) CNRS–UMR 8520 Université de Lille 59652 Villeneuve d’Ascq France DRDO-Industry-Academia Centre of Excellence (DIA-CoE) Indian Institute of Technology Delhi New Delhi 110016 India School of Electrical Engineering and Computer Science University of Queensland Brisbane QLD 4072 Australia Cambridge Consultants Boston MA United States of America Department of Physics Indian Institute of Technology Delhi New Delhi 110016 India Physics Department Lund University SE-221 00 Lund Sweden Institute of Micromechanics and Photonics Warsaw University of Technology 8 Sw. Andrzeja Boboli St 02-525 Warsaw Poland Central Office of Measures 2 Elektoralna St. 00-139 Warsaw Poland Centre for Nutrition and Food Sciences Queensland Alliance for Agriculture and Food Innovation (QAAFI) The University of Queensland St.Lucia Brisbane QLD 4072 Australia Non-Destructive Spectral Sensors Unit Department of Animal Production Fa

Photonics technologies play a crucial role in driving technological advancements within the agrifood industry, aiming to deliver a sustainable food and agriculture, and offering healthy, nutritious and safe food for all of us. Particularly, optical sensors and imaging systems, together with machine-learning processing and advanced lighting, play a pivotal role in monitoring crop and soil health with unprecedented precision, while safeguarding the food supply chain. This roadmap aims to provide an overview of the state-of-the-art photonics technologies benefitting agrifood applications, including a view on their current limitations, challenges and future potential, while addressing practical case studies. Future trends towards multimodal sensors and sensor fusion, artificial intelligence and digital twins, miniaturization and controlled farming are highlighted. The revolutionizing capabilities of the photonics technologies are indicated, inspiring future applications and developments, and paving the way towards optimized resource utilization, increased crop yields, stopping land degradation and reduction of food waste.

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