[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128758-en":3,"doc-seo-128758-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128758,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography","Rising interest in spectral imaging has accelerated research on extracting richer information from aerial photography, including data captured via UAVs. By integrating machine learning and deep learning, spectral images have shown strong performance for image classification, supporting faster and more accurate recognition in agricultural settings. This survey reviews multispectral and hyperspectral image studies and maps classification applications across plants, grains, fruits, and vegetables, while evaluating methods used for hyperspectral classification. Results highlight deep learning and support vector machines as frequently applied approaches, alongside key limitations and practical issues.","Tech Science Press  \n| DOI: 10.32604/cmc.2024.045101\u003Cbr>REVIEW | \u003Cbr> |\n| --- | --- |\n| A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography\u003Cbr>Usman Khan1 , Muhammad Khalid Khan1 , Muhammad Ayub Latif1 , Muhammad Naveed1 , 2 , *, Muhammad Mansoor Alam2 , 3 ,4 , Salman A. Khan1 and Mazliham Mohd Su’ud2 , *\u003Cbr>1 College of Computing and Information Sciences, Karachi Institute of Economics and Technology, Karachi, 75190, Pakistan 2 Faculty of Computing and Informatics (FCI), Multimedia University, Cyberjaya, 63100, Malaysia\u003Cbr>3 Faculty of Computing, Riphah International University, Islamabad, 46000, Pakistan\u003Cbr>4 Faculty of Engineering and Information Technology, School of Computer Science, University of Technology Sydney, Sydney, Australia\u003Cbr>*[Corresponding Authors: Muhammad Naveed. Email: naveed@kiet.edu.pk](Corresponding Authors: Muhammad Naveed. Email: naveed@kiet.edu.pk); Mazliham Mohd Su’ud.\u003Cbr>[Email: mazliham@mmu.edu.my](Email: mazliham@mmu.edu.my)\u003Cbr>Received: 17 August 2023 Accepted: 10 November 2023 Published: 26 March 2024\u003Cbr>\u003Cbr>ABSTRACT\u003Cbr>Recently, there has been a notable surge of interest in scientific research regarding spectral images. The potential of these images to revolutionize the digital photography industry, like aerial photography through Unmanned Aerial Vehicles (UAVs), has captured considerable attention. One encouraging aspect is their combination with machine learning and deep learning algorithms, which have demonstrated remarkable outcomes in image classification. Asa result of this powerful amalgamation, the adoption of spectral images has experienced exponential growth across various domains, with agriculture being oneofthe prominent beneficiaries. This paper presentsan extensive survey encompassing multispectral and hyperspectral images, focusing on their applications for classification challenges in diverse agricultural areas, including plants, grains, fruits, and vegetables. By meticulously examining primary studies, we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use. Additionally, our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context. The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture. Nevertheless, we also shed light on the various issues and limitations of working with spectral images. This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.\u003Cbr>KEYWORDS\u003Cbr>Machine learning; deep learning; unmanned aerial vehicles; multi-spectral images; image recognition; object detection; hyperspectral images; aerial photography |  |\n\nThis work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \n2968 CMC, 2024, vol.78, no.3  \n1 Introduction  \nAn image constitutes an array of pixels originating from diverse sources, such as standard specialized cameras or mobile phones. The significance of imaging extends across various domains, particularly in detection and recognition tasks. Image processing techniques are the initial step for computational methods by extracting valuable information from the provided image. By utilizing this extracted information, computers can attain heightened intelligence in tackling real-world and intricate challenges [1] . One of the limitations commonly found in traditional image processing techniques is the inability to acquire spatial and spectral information for various objects [2] . Spectroscopy investigates the behavior","cbCaiteF3upMvLNz","https://ap.wps.com/l/cbCaiteF3upMvLNz","pdf",1339354,1,34,"English","en",105,"# Abstract\n# Introduction\n## Spectral imaging and limitations of traditional imaging\n## Spectral sensors, spectral bands, and hyperspectral imaging\n## Electromagnetic wavelength ranges and why spectral imaging adds information","[{\"question\":\"What does the survey focus on in agricultural spectral imaging?\",\"answer\":\"The survey examines how multispectral and hyperspectral images are used for classification tasks in agriculture, including plants, grains, fruits, and vegetables. It emphasizes machine learning methods for hyperspectral image classification in agricultural contexts.\"},{\"question\":\"Why are spectral images valuable for aerial photography and UAVs?\",\"answer\":\"Spectral cameras capture information across selective wavelength ranges beyond visible colors, including infrared bands. This allows computers to obtain more detailed spectral information than standard imaging, improving classification and recognition.\"},{\"question\":\"Which methods are reported as widely used for hyperspectral image classification in agriculture?\",\"answer\":\"The findings identify deep learning and support vector machines as widely employed approaches for hyperspectral image classification in agriculture. The survey also discusses issues and limitations involved in using spectral images.\"}]","A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography | PDF",1786003152,86,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-systematic-literature-review-of-machine-learning-and-deep-learning-approaches-for-spectral-image-classification-in-agricultural-applications-using-aerial-photography","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-systematic-literature-review-of-machine-learning-and-deep-learning-approaches-for-spectral-image-classification-in-agricultural-applications-using-aerial-photography/128758/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-06",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the survey focus on in agricultural spectral imaging?","Question",{"text":75,"@type":76},"The survey examines how multispectral and hyperspectral images are used for classification tasks in agriculture, including plants, grains, fruits, and vegetables. It emphasizes machine learning methods for hyperspectral image classification in agricultural contexts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are spectral images valuable for aerial photography and UAVs?",{"text":80,"@type":76},"Spectral cameras capture information across selective wavelength ranges beyond visible colors, including infrared bands. This allows computers to obtain more detailed spectral information than standard imaging, improving classification and recognition.",{"name":82,"@type":73,"acceptedAnswer":83},"Which methods are reported as widely used for hyperspectral image classification in agriculture?",{"text":84,"@type":76},"The findings identify deep learning and support vector machines as widely employed approaches for hyperspectral image classification in agriculture. 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