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    <title>DSpace Coleção: Resumo em anais de congresso (CNPTIA)</title>
    <link>https://www.alice.cnptia.embrapa.br/alice/handle/item/198</link>
    <description>Resumo em anais de congresso (CNPTIA)</description>
    <pubDate>Fri, 24 Apr 2026 04:57:20 GMT</pubDate>
    <dc:date>2026-04-24T04:57:20Z</dc:date>
    <item>
      <title>Center for science for development of digital agriculture: Semear Digital, Brazil.</title>
      <link>https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186344</link>
      <description>Título: Center for science for development of digital agriculture: Semear Digital, Brazil.
Autoria: BOLFE, E. L.; ROMANI, L. A. S.; BARBEDO, J. G. A.; MASSRUHÁ, S. M. F. S.
Conteúdo: Projections of world population show that there will be a greater demand for food, fiber and energy, requiring an increase in agricultural productivity, cost reduction and sustainable use of natural resources. Thus, it is essential to consider the process of digital transformation in the countryside, with the introduction of equipment and sensors to collect and generate data in amounts that easily surpass the human processing capacity. In this context, “Semear Digital” is a Brazilian research center designed to enhance agricultural productivity and sustainability through digital technologies and connectivity solutions. The center prioritizes research in inclusive digital technologies in six axis, integrating small and medium scale producers, and operates under the coordination of the Brazilian Agricultural Research Corporation (Embrapa), funded by the São Paulo Research Foundation (FAPESP), and with a collaborative consortium of Research, Development, and Innovation (RD&amp;I) institutions.</description>
      <pubDate>Thu, 01 Jan 2026 00:00:00 GMT</pubDate>
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      <dc:date>2026-01-01T00:00:00Z</dc:date>
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    <item>
      <title>Advancing coffee management mapping through multisensor data and multistep ensemble learning.</title>
      <link>https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186342</link>
      <description>Título: Advancing coffee management mapping through multisensor data and multistep ensemble learning.
Autoria: PARREIRAS, T. C.; BOLFE, E. L.; FURUYA, D. E. G.
Conteúdo: Despite the advances, accurately identifying recently renovated and skeletonized coffee areas remains a challenge, as their altered canopy structure and reduced vigor produce spectral signatures similar to those of fallow or non-coffee areas. To address these limitations, upcoming research will focus on leveraging a space-time hybrid approach with deep learning and surface phenology modeling. Specifically, we plan to implement a workflow combining the spatial detail of Sentinel-2 with the temporal continuity of HLS.</description>
      <pubDate>Thu, 01 Jan 2026 00:00:00 GMT</pubDate>
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      <dc:date>2026-01-01T00:00:00Z</dc:date>
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    <item>
      <title>From satellites to smartphones: opportunities for digital agriculture in apple orchards.</title>
      <link>https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186341</link>
      <description>Título: From satellites to smartphones: opportunities for digital agriculture in apple orchards.
Autoria: FURUYA, D. E. G.; BOLFE, E. L.; PARREIRAS, T. C.
Conteúdo: Mobile phones images of Fuji and Gala apples are being used to evaluate the performance of neural networks for fruit detection, which can serve as the basis for developing future mobile applications not only for fruit detection but also for the assessment of different characteristics and parameters, such as size, color, or quality. Such applications may support farmers and technicians by offering practical and low-cost tools for monitoring orchard conditions.</description>
      <pubDate>Thu, 01 Jan 2026 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186341</guid>
      <dc:date>2026-01-01T00:00:00Z</dc:date>
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    <item>
      <title>Produtividade em cana-de-açucar em variedades convencionais e tipo cana energia sob diferentes doses de potassio.</title>
      <link>https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186019</link>
      <description>Título: Produtividade em cana-de-açucar em variedades convencionais e tipo cana energia sob diferentes doses de potassio.
Autoria: SILVA, F. C. da; RAIZER, A. J.; CARVALHO, M. L.; DIAS, V. G.; CRISTOFOLETI, D.; CASTRO, A. de
Conteúdo: A cana-de-açúcar é cultura de destaque no Brasil, pela importância socioeconômica e como fonte bioenergética, ocupando área de colheita de 8,5 milhões de hectares e produção de 655 milhões de toneladas, em 2021. O potássio se destaca dentre os nutrientes utilizados na cultura, sendo o nutriente exportado em maior quantidade, além de influenciar na sua qualidade. O objetivo do estudo foi avaliar, em vários ciclos, doses crescentes de potássio aplicadas na cana-de-açúcar, bem como o comportamento de variedades convencionais e de cana energia.</description>
      <pubDate>Sun, 01 Jan 2023 00:00:00 GMT</pubDate>
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      <dc:date>2023-01-01T00:00:00Z</dc:date>
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