Resultados de búsqueda - Machine design.

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  1. Inoculant machine por Technical Centre for Agricultural and Rural Cooperation

    Publicado 1987
    “…This self-contained unit is designed to produce the rhizobium bacteria needed to enable leguminous plants to fix...…”
    Enlace del recurso
    News Item
  2. Harvesting and In-Field Sorting of Citrus with a Self-Propelled Machine por Gutiérrez, Abelardo, Blasco, José, Chueca, Patricia, Garcerá, Cruz, Alegre, Santiago, Lopez, S., Cubero, Sergio, Moltó, Enrique

    Publicado 2017
    “…Limited energy and space availability have been critical issues for the electronic and mechanical designs. All the subsystems are commanded by a central unit that can be easily adapted to each orchard and is operated by one worker. …”
    Enlace del recurso
    Objeto de conferencia
  3. Leveraging unsupervised machine learning to examine women's vulnerability to climate change por Caruso, German, Mueller, Valerie, Villacis, Alexis

    Publicado 2024
    “…We provide an application of machine learning to identify the distributional consequences of climate change in Malawi. …”
    Enlace del recurso
    Journal Article
  4. Sample Earth: Machine-Learning–Ready Land-Cover Reference Dataset por Vantalon, Thibaud, Luong, Phuong Thi, Perez Escobar, Jorge Andres, Tello Dagua, Jhon Jairo, Phan, Trong Van, Nguyen, Hang, Hong Nguyen, Hoa Nguyen, Reymondin, Louis

    Publicado 2025
    “…This combined approach of expert interpretation, localized training, and structured data management ensured a high-quality, consistent, and machine-learning–ready dataset suitable for land-cover mapping and model training workflows.…”
    Enlace del recurso
    Conjunto de datos
  5. Multispectral inspection of citrus in real-time using machine vision and digital signal processors por Aleixos, Nuria, Blasco, José, Navarron, F., Moltó, Enrique

    Publicado 2017
    “…For these reasons, automatic inspection means, as machine vision, are a priority in Spain, in order to ensure products with an excellent quality. …”
    Enlace del recurso
    Artículo
  6. A map of global peatland extent created using machine learning (Peat-ML) por Melton, Joe R., Chan, Ed, Millard, Koreen, Fortier, Matthew, Winton, R. Scott, Martin López, Javier Mauricio, Cadillo-Quiroz, Hinsby, Kidd, Darren, Verchot, Louis V.

    Publicado 2022
    “…The first estimate used the training data in a blocked leave-one-out cross-validation strategy designed to minimize the influence of spatial autocorrelation. …”
    Enlace del recurso
    Journal Article
  7. Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision por Blasco, José, Cubero, Sergio, Gómez-Sanchís, Juan, Mira, P., Moltó, Enrique

    Publicado 2017
    “…The prototype is composed of three units, which are designed to singulate the objects to allow them be inspected individually and sorted. …”
    Enlace del recurso
    Artículo
  8. Reviving the Ganges water machine: accelerating surface water and groundwater interactions in the Ramganga sub-basin por Surinaidu, L., Muthuwatta, Lal P., Amarasinghe, Upali A., Jain, S.K., Kumar, S., Singh, S.

    Publicado 2016
    “…Reviving the Ganges Water Machine (GWM), coined 40 years ago, is the most opportune solution for mitigating the impacts of recurrent droughts and floods in the Ganges River Basin in South Asia. …”
    Enlace del recurso
    Journal Article
  9. Development of an automatic fish feeder por Ozigbo, E., Anyadike, C., Gbadebo, F., Okechukwu, R.U.

    Publicado 2013
    “…An automatic fish feeder was designed, fabricated and tested. It eliminates major problems associated with manual feeding in aquaculture. …”
    Enlace del recurso
    Journal Article
  10. Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery por Pizarro, S., Pricope, N.G., Figueroa, D., Carbajal, C., Quispe, M., Vera, J., Alejandro, L., Achallma, L., Gonzalez, I., Salazar, W., Loayza, H., Cruz, J., Arbizu, C.I.

    Publicado 2023
    “…To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). …”
    Enlace del recurso
    Journal Article
  11. Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report por Ileperuma, Kaveesha, Jampani, Mahesh, Sellahewa, Uvindu, Panjwani, Shweta, Amarnath, Giriraj

    Publicado 2023
    “…The current report presents a machine learning model developed to predict malaria prevalence based on rainfall patterns, specifically tailored to different regions within Senegal. …”
    Enlace del recurso
    Informe técnico
  12. Integrating crop models and machine learning for projecting climate change impacts on crops in data-limited environments por Alimagham, Seyyedmajid, van Loon, Marloes P, Ramirez Villegas, Julian, Berghuijs, Herman N.C., Rosenstock, Todd Stuart, van Ittersum, Martin K.

    Publicado 2025
    “…Crop growth models and machine learning (ML) are often used, but their effectiveness is limited by data availability, precision, and geographic coverage in SSA. …”
    Enlace del recurso
    Journal Article
  13. Estimation of soybean grain yield from multispectral high-resolution UAV data with machine learning models in west Africa por Alabi, T.R., Abebe, A.T., Chigeza, G., Fowobaje, K.R.

    Publicado 2022
    “…Integrating new varietal evaluation approaches based on advanced phenotyping techniques into IITA's soybean breeding program is crucial for designing efficient crop genetic improvement techniques. …”
    Enlace del recurso
    Journal Article
  14. Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia por Mann, Michael L., Warner, James, Malik, Arun S.

    Publicado 2019
    “…Timely and accurate agricultural impact assessments for droughts are critical for designing appropriate interventions and policy. These assessments are often ad hoc, late, or spatially imprecise, with reporting at the zonal or regional level. …”
    Enlace del recurso
    Journal Article
  15. Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia por Mann, Michael L., Malik, Arun S., Warner, James

    Publicado 2018
    “…Timely and accurate agricultural impact assessments for droughts are critical for designing appropriate interventions and policy. These assessments are often ad hoc, late, or spatially imprecise, with reporting at the zonal or regional level. …”
    Enlace del recurso
    Artículo preliminar

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