Proceedings
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| Filter results4 paper(s) found. |
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1. 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 |
2. Variable Rate Application to Improve Cro Protection in Orchards and Vineyards. Prescription Maps and Satellites to Accomplish EU Farm to Fork StrategyAccurate canopy characterization is crucial for a targeted application of plant protection products following variable rate application (VRA) concept. Remote sensing offers a robust and rapid monitoring tool that allows determining the characteristics of the vegetation from aerial platforms at different spatial resolutions. Previous work have demonstrated that drone-based imagery can be used to estimate canopy height, width, and canopy volume accurately enough to allow a full automation of VRA... E. Gil, F. Garcia-ruíz, J. Biscamps, R. Salcedo, J. Campos |
3. How Does an Autonomous Tractor See the World... G. Bansal |
4. Operational Satellite Weed Detection Across 14,000 Sugarcane Fields: Lessons in Temporal Feature DesignWeed infestations in sugarcane (Saccharum officinarum L.) can reduce yields by 20–60% depending on species composition and management timing, yet operational weed management at scale remains an unsolved challenge. Existing studies typically cover tens of fields; scaling to thousands introduces challenges in processing throughput, ground truth scarcity, and feature design. This work describes the development and operational deployment of a satellite-based weed detection system covering... C. Ferraz, R. Fortes, M. Cabrera Dengra, J. Poli, E. Bernardes Júnior, A. Do Vale Dondo |