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Machine Learning Forecast of Soybean Yields on South Brazil

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Abstract

This article investigates the problem of agricultural yield prediction, including that of soybeans. Soya is a very nutritious legume and one of the species producing the most protein per hectare. It is used in particular as a source of protein in animal feed and as an oilseed. However, agricultural systems are dependent on climatic variability, and farmers must deal with this factor to optimize their activities. This article describes a method for predicting soybean yields based on machine learning. The comparative study shows that one can obtain forecasts with less than 2% margin of error using the Random Forest algorithm. In addition, the results obtained in this study can be extended to many other crops such as maize or rice.
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Dates and versions

hal-03647743 , version 1 (20-04-2022)

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  • HAL Id : hal-03647743 , version 1

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Lilian Hollard, Angelica Durigon, Luiz Angelo Steffenel. Machine Learning Forecast of Soybean Yields on South Brazil. Workshop on Edge AI for Smart Agriculture (EAISA 2022), Jun 2022, Biarritz, France. ⟨hal-03647743⟩

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