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| Filter results4 paper(s) found. |
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1. Modelling 'Concord' Berry Weight DynamicsThe growth and development of Concord (Vitis labruscana Bailey) depends on internal and external factors. As a result, both vegetative and reproductive cycles of Concord vary based on growing season and vine status. Fresh berry weight also fluctuates depending on the growing season and location of the vineyard. Knowledge of berry weight dynamics across growing season is essential to accurately predict final yield at harvest based on early season crop estimates. The main objective of this study... G. Badr, T.R. Bates |
2. AgDataBox-IoT Application Development for Agrometeorogical Stations in Smart FarmCurrently, Brazil is one of the world’s largest grain producers and exporters. Brazil produced 125 million tons of soybean in the 2019/2020 growing season, becoming the world’s largest soybean producer in 2020. Brazil’s economic dependence on agribusiness makes investments and research necessary to increase yield and profitability. Agriculture has already entered its 4.0 version, also known as digital agriculture, when the industry has entered the 4.0 era. This new paradigm uses... A. Hachisuca, E.G. Souza, E. Mercante, R. Sobjak, D. Ganascini, M. Abdala, I. Mendes, C. Bazzi, M. Rodrigues |
3. Use of MLP Neural Networks for Sucrose Yield Prediction in SugarbeetINTRODUCTION Sugar beet is one of the more technified agro industries in Spain. In the last years, it has leaded as well the digital transformation with the objective of maintaining sugar beet competitivity both national and internationally. Among other lines, very high potential has been identified in determining the sucrose content using a combination of Artificial Intelligence and Remote Sensing. This work presents the conclusions of an extensive data acquisition task, creation of... M. Cabrera dengra, C. Ferraz pueyo, V. Pajuelo madrigal, L. Moreno heras, G. Inunciaga leston, R. Fortes |
4. Site-specific Evaluation of Sensor-based Winter Wheat Nitrogen Tools Via On-farm ResearchCrop producers face the challenge of optimizing high yields and nitrogen use efficiency (NUE) in their agricultural practices. Enhancing NUE has been demonstrated by adopting digital agricultural technologies for site-specific nitrogen (N) management, such as remote-sensing based N recommendations for winter wheat. However, winter wheat fields are often uniformly fertilized, disregarding the inherent variability within the fields. Thus, an on-farm evaluation of sensor-based N tools is needed to... J. Cesario pinto, L. Thompson, N. Mueller, T. Mieno, L. Puntel, P. Paccioretti, G. Balboa |