Towards more efficient agricultural practices via transformer-based crop type classification
This work addresses the need for efficient agricultural mapping to support policy and research, but it is incremental as it builds on existing transformer and meta-learning methods.
The authors tackled crop type classification using satellite imagery in Mexico, achieving accurate binary classification with a transformer model and proposing meta-learning for improved multi-class classification.
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.