Methodology for multi-temporal prediction of crop rotations using recurrent neural networks

In a context of growing demand for food and the scarcity of natural resources, the development of more sustainable agriculture is imperative. This means it is necessary to limit the environmental impact of agricultural activities on soil and water and to be mindful of the carbon footprint, while maintaining crop yields and economic benefits for producers. Crop rotation is a valuable tool in sustainable agriculture, but this technique has to be appropriately coupled with sustainable fertilization plans to optimize crops. The proposed methodology uses recurrent neural networks (RNN); more precisely, LSTMs, in a Seq2Seq architecture, to predict the most probable scenarios of crop rotations to be exploited in a field in subsequent growing seasons, according to cropping habits. The output can be used in crop models to build a decision support system for greater sustainability in agricultural production by allowing producers to choose the strategy that offers the best compromise between profitability and environmental impact.

Mots clés

• agriculture 4.0
• crop rotation
• deep learning
• Seq2Seq
• LSTM