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- W3098593916 abstract "Accurately determining crop growth progress and crop yields at field-scale can help farmers estimate their net profit, enable insurance compa-nies to ascertain payouts, and help in ensuring food security. At field scales, the troika of management, soil and weather combine to impact crop growth pro-gress, and this progress can be monitored in-season using satellite data. Here, we use satellite derived metrics, from both optical and radar satellites, and ma-chine learning models to model field-scale crop yields for over 3,000 Soybean and Wheat in Argentina. We compare several machine learning models and our results show the promise of combining mixed effect models with non-parametric models in improving yield modeling capabilities. We also demon-strate the utility of specific satellite derived metrics and extracted features in improving model performance and show that our approach can explain greater than 70% of the variation in yields while remaining generalizable across crops and agro-ecological zones." @default.
- W3098593916 created "2020-11-23" @default.
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- W3098593916 date "2020-11-09" @default.
- W3098593916 modified "2023-10-01" @default.
- W3098593916 title "Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina" @default.
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- W3098593916 doi "https://doi.org/10.31223/x52595" @default.
- W3098593916 hasPublicationYear "2020" @default.
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