Search Results - Machine design.

Design, development, and performance evaluation of a power-operated jute fiber extraction machine by Karim, Md. Rejaul, Hoque, Muhammad Arshadul, Chawdhury, Alamgir, Faruk-Ul-Islam, Ahmed, Sharif, EL Sabagh, Ayman, Hossain, Akbar

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Design and Development of a Digital Platform for Soil Health Monitoring and Management by Patil, Mukund, Burry, Sukanya, Katakamsetti, Nagendra, Choudhari, Pushpajeet L., Perumal, Cuba, Gogumalla, Pranuthi, Rozario, Gilbert, Shaik, Jameeruddin, Rupavatharam, Srikanth

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Inoculant machine by Technical Centre for Agricultural and Rural Cooperation

“…This self-contained unit is designed to produce the rhizobium bacteria needed to enable leguminous plants to fix...…”
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Improved machine for weeding fields by Technical Centre for Agricultural and Rural Cooperation

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Harvesting and In-Field Sorting of Citrus with a Self-Propelled Machine by Gutiérrez, Abelardo, Blasco, José, Chueca, Patricia, Garcerá, Cruz, Alegre, Santiago, Lopez, S., Cubero, Sergio, Moltó, Enrique

“…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. …”
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Leveraging unsupervised machine learning to examine women's vulnerability to climate change by Caruso, German, Mueller, Valerie, Villacis, Alexis

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

“…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.…”
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Multispectral inspection of citrus in real-time using machine vision and digital signal processors by Aleixos, Nuria, Blasco, José, Navarron, F., Moltó, Enrique

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

“…The first estimate used the training data in a blocked leave-one-out cross-validation strategy designed to minimize the influence of spatial autocorrelation. …”
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Early detection of infection by Penicillium digitatum in oranges using hyperspectral imaging and machine learning by Castillo-Gironés, Salvador, Gómez-Sanchis, Juan, López-Chulia, Marina, Guirao, Alberto, Palou, Lluís, Souza, Ricardo-Lima-de, Blasco, José

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Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision by Blasco, José, Cubero, Sergio, Gómez-Sanchís, Juan, Mira, P., Moltó, Enrique

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

“…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. …”
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Development of an automatic fish feeder by Ozigbo, E., Anyadike, C., Gbadebo, F., Okechukwu, R.U.

“…An automatic fish feeder was designed, fabricated and tested. It eliminates major problems associated with manual feeding in aquaculture. …”
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Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery by 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.

“…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). …”
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Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report by Ileperuma, Kaveesha, Jampani, Mahesh, Sellahewa, Uvindu, Panjwani, Shweta, Amarnath, Giriraj

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

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

“…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. …”
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Controlling groundwater through smart card machines: the case of water quotas and pricing mechanisms in Gansu Province, China by Aarnoudse, E., Bluemling, B.

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Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia by Mann, Michael L., Warner, James, Malik, Arun S.

“…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. …”
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Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia by Mann, Michael L., Malik, Arun S., Warner, James

“…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. …”
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