Proceedings
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| Filter results2 paper(s) found. |
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1. Forecasting Crop Yield Using Multi-Layered, Whole-Farm Data Sets and Machine LearningThe ultimate goal of Precision Agriculture is to improve decision making in the business of farming. Many broadacre farmers now have a number of years of crop yield data for their fields which are often augmented with additional spatial data, such as apparent soil electrical conductivity (ECa), soil gamma radiometrics, terrain attributes and soil sample information. In addition there are now freely available public datasets, such as rainfall, digital soil maps and archives of satellite remote... P. Filippi, E.J. Jones, M. Fajardo, B.M. Whelan, T.F. Bishop |
2. Soil-Sensing-Based Irrigation Decision Modeling for Greenhouse Tomato Crops Using Machine LearningGlobal agriculture faces increasing pressure to optimize water-use efficiency, particularly for high-demand crops like tomato (Solanum lycopersicum). Tomato is among the most widely consumed vegetables worldwide, playing a central role in global food systems. From an agronomic perspective, tomato crops are highly sensitive to water availability and distribution, requiring precise irrigation management to ensure sustainable production and high-quality yields. In controlled environments such as... P. Guerra, A.R. Raucci, S.A. Gutierrez , J.F. Botero, C. Kamienski, F.M. Campos De Oliveira |