[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128400-en":3,"doc-seo-128400-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128400,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Analysis and classification of the cabruca agroforestry system with machine learning in the Atlantic Forest Biome - Brazil","The absence of labeled and spectrally similar cabruca systems in Atlantic Forest databases for the Almada, Cachoeira, and Una river basins limits accurate area classification. This study analyzes cabruca classification using satellite images of different resolutions and machine-learning models. The workflow defines land cover and land-use classes, acquires and processes satellite imagery with Python algorithms, and applies supervised classification. Results show cropping algorithms preserve essential geographic information, while a neural network reaches 92% accuracy across spatial-resolution diversity, though outputs should be treated as estimates due to confusion with Dense Ombrophilous Forest.","Analysis and classification of the cabruca agroforestry system with machine learning in the Atlantic Forest Biome  \n– Brazil  \nAnálise e classificação do sistema agroflorestal cabruca com aprendizagem de máquina no Bioma Mata Atlântica –  \nBrasil  \nVinícius de Amorim Silva *, Hercules da Silva Carvalho **, Paulo Sérgio Vila Nova Souza ***  \n* Center for Training in Technosciences and Innovation, Federal University of Southern Bahia - UFSB, [vinicius@ufsb.edu.br](vinicius@ufsb.edu.br)  \n** Center for Training in Technosciences and Innovation, Federal University of Southern Bahia – UFSB, [hercules.carvalho@cja.ufsb.edu.br](hercules.carvalho@cja.ufsb.edu.br)  \n*** Center for Training in Agroforestry Sciences, Federal University of Southern Bahia - UFSB, [paulosvn@gfe.ufsb.edu.br](paulosvn@gfe.ufsb.edu.br)  \n[http://dx.doi.org/10.5380/raega.v64i1.101334](http://dx.doi.org/10.5380/raega.v64i1.101334)  \nAbstract  \nThe absence of labeling and spectral similarity of cabruca systems in the Atlantic Forest biome for the Almada, Cachoeira, and Una River basins in the databases hinders the proper classification of these areas. Thus, this study aims to analyze the classification of cabruca areas in the mentioned basins through the use of images of different resolutions and machine learning. The methodology includes: determining land cover and land use classes, acquiring satellite images, processing the images with algorithms in Python, and evaluating supervised classification. The results show that cropping algorithms are effective in processing satellite images, preserving essential geographic information. Furthermore, the Neural Network model achieved an accuracy of 92%, regardless of the diversity of spatial resolutions. However, it is recommended to use the classification results of cabruca areas as estimates, due to the difficulty of the algorithms in distinguishing these areas from Dense Ombrophilous Forest.  \nKeywords:  \nArtificial Intelligence, Geoprocessing, Shaded Cocoa, Biodiversity Conservation.  \nResumo  \nA ausência de rotulagem e similaridade espectrais dos sistemas cabruca no bioma Mata Atlântica para as bacias hidrográficas dos rios Almada, Cachoeira e Una nos bancos de dados dificulta a classificação adequada dessas áreas. Assim, este trabalho objetiva analisar a classificação das áreas de cabruca nas referidas bacias por meio do uso de imagens de diferentes resoluções e aprendizado de máquina. A metodologia inclui: a determinação das classes de cobertura e uso da terra, a obtenção de imagens de satélite, o processamento das imagens com algoritmos em Python  \ne a avaliação da classificação supervisionada. Os resultados mostram que os algoritmos de recortesão eficazes no processamento das imagens de satélite, preservando informações geográficasessenciais. Além disso, o modelo de Redes Neurais alcançou uma acurácia de 92%, independe da diversidade de resoluções espaciais. No entanto, recomenda-se usar os resultados da classificação das áreas de cabruca como estimativas, devido à dificuldade dos algoritmos em distinguir essas áreas da Floresta Ombrófila Densa.  \nPalavras-chave:  \nInteligência Artificial, Geoprocessamento, Cacau sombreado, Conservação da biodiversidade.  \nI. INTRODUCTION  \nThe classification of cabruca areas is essential for assessing the impact of agricultural practices on biodiversity conservation and the maintenance of ecosystem services. Cabruca, an agroforestry system in the southern region of Bahia, represents a multifunctional strategy for cacao production and conservation of forest remnants (Valadares, 2016; Embrapa, 2021; Xavier; Nascimento Jr; Chiapetti, 2021a) . The practice of cabruca, which involves the cultivation of cacao under the shade of dense ombrophilous vegetation, stands out, in areas where forests are scarce and fragmented, as a sustainable approach to agriculture and environmental conservation of native species (Xavier; Nascimento Jr; Chiapetti, 2021b) .  \nIn this way, geoprocessing bec","cbCairJj6R45P5zn","https://ap.wps.com/l/cbCairJj6R45P5zn","pdf",2509980,2,1,21,"English","en",105,"# Introduction\n## Cabruca’s role in biodiversity conservation\n## Geoprocessing and remote sensing challenges\n## Motivation for machine learning-based classification","[{\"question\":\"Why is cabruca area classification difficult for these river basins?\",\"answer\":\"It is hindered by the lack of labeling and the spectral similarity between cabruca systems and Dense Ombrophilous Forest in available databases.\"},{\"question\":\"What methodology is used to classify cabruca areas?\",\"answer\":\"The study determines land cover and land-use classes, obtains satellite images at different resolutions, processes them with Python algorithms, and evaluates supervised classification.\"},{\"question\":\"How accurate is the proposed neural network model?\",\"answer\":\"The neural network achieves 92% accuracy, even across diverse spatial resolutions.\"}]","Analysis and classification of the cabruca agroforestry system with machine learning in the Atlantic Forest Biome - Brazil | PDF",1785947309,53,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"analysis-and-classification-of-the-cabruca-agroforestry-system-with-machine-learning-in-the-atlantic-forest-biome-brazil","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/analysis-and-classification-of-the-cabruca-agroforestry-system-with-machine-learning-in-the-atlantic-forest-biome-brazil/128400/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is cabruca area classification difficult for these river basins?","Question",{"text":76,"@type":77},"It is hindered by the lack of labeling and the spectral similarity between cabruca systems and Dense Ombrophilous Forest in available databases.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What methodology is used to classify cabruca areas?",{"text":81,"@type":77},"The study determines land cover and land-use classes, obtains satellite images at different resolutions, processes them with Python algorithms, and evaluates supervised classification.",{"name":83,"@type":74,"acceptedAnswer":84},"How accurate is the proposed neural network model?",{"text":85,"@type":77},"The neural network achieves 92% accuracy, even across diverse spatial resolutions.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]