Towards more efficient agricultural practices via transformer-based crop type classification

• URL: http://arxiv.org/abs/2411.02627v1
• Date: Mon, 04 Nov 2024 21:38:02 GMT
• Title: Towards more efficient agricultural practices via transformer-based crop type classification
• Authors: E. Ulises Moya-Sánchez, Yazid S. Mikail, Daisy Nyang'anyi, Michael J. Smith, Isabella Smythe,
• Abstract summary: It is possible to accurately classify crops from time series derived from Sentinel 1 and 2 satellite imagery in Mexico. We propose further development of this method with the goal of accurate multi-class crop classification in Jalisco, Mexico.
• Score: 0.0
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• Abstract: Machine learning has great potential to increase crop production and resilience to climate change. Accurate maps of where crops are grown are a key input to a number of downstream policy and research applications. In this proposal, we present preliminary work showing that it is possible to accurately classify crops from time series derived from Sentinel 1 and 2 satellite imagery in Mexico using a pixel-based binary crop/non-crop time series transformer model. We also find preliminary evidence that meta-learning approaches supplemented with data from similar agro-ecological zones may improve model performance. Due to these promising results, we propose further development of this method with the goal of accurate multi-class crop classification in Jalisco, Mexico via meta-learning with a dataset comprising similar agro-ecological zones.

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