Proceedings
Authors
| Filter results2 paper(s) found. |
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1. Early Yield Estimation in Hass Avocado Using Ecophysiological Variables and Machine LearningThis study evaluated the ability of machine learning models to estimate yield in mature Hass avocado trees (>5 years), using ecophysiological variables measured with MultispeQ v2.0 (RIDES 2.1 protocol) and electrical capacitance (1 Hz). The study was conducted at Pan de Azúcar farm (Villahermosa, Tolima, Colombia; 1,565 m a.s.l., Andisols) on 60 trees, with data collected across four phenological stages (fruit development, fruit maturation, leaf and shoot growth, and pre-flowering) and... D. Rayo Álvarez, P.J. Murillo Sandoval, A.E. Darghan Contreras, D.F. Conejo Rodriguez |
2. Satellite Embedding-Based Corn Yield Prediction Using AutoML and Explainable AIAccurate, spatially explicit yield mapping underpins many precision agriculture decisions (e.g., variable-rate inputs and zone management), yet reliable yield monitor data are not always available and can be difficult to standardize across operations. Satellite-based yield models are often built from hand-crafted vegetation indices or phenology metrics, which may limit transferability across fields and years. Here, we evaluated a pixel-level corn yield prediction workflow that uses Satellite Embedding... V.S. Silva, E.S. Silva, D.O. Silva, M.F. Oliveira, A.C. Tavares, R.P. Negrini, L.A. Mendes |