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

arXiv: https://arxiv.org/abs/2411.02627 · HTML: https://mlsanity.com/paper/2411.02627/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 1 of 1)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)

## Abstract (arXiv)

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.