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- W2990404718 abstract "Abstract As our agricultural systems are expected to sustain a population of around 10 billion by 2050, and increase their production by nearly 50% globally, agricultural producers, intermediaries, distributors, and resellers are looking at novel alternatives to improve their efficiencies and reduce their risk exposure. For example, the producers aim at maximizing yield while controlling costs and to have a better understanding of the drivers that affect quantity, quality, and time to harvest different crops. These drivers are often climate or weather related and might include extreme events such as heat waves, cold spells, hail events, higher than usual rainfall, and environmental conditions that are favorable to different pests, as well as bio-geophysical characteristics of their terroir and crop. On the other hand, advances in machine learning (ML) are progressively being incorporated to support those management decisions. This chapter elaborates on classification, detection, and forecasting using ML." @default.
- W2990404718 created "2019-12-05" @default.
- W2990404718 creator A5008112770 @default.
- W2990404718 date "2020-01-01" @default.
- W2990404718 modified "2023-10-17" @default.
- W2990404718 title "Machine learning applications for agricultural impacts under extreme events" @default.
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- W2990404718 doi "https://doi.org/10.1016/b978-0-12-814895-2.00007-0" @default.
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