Towards more efficient agricultural practices via transformer-based crop type classification (Proposals Track)
Isabella Smythe (Columbia University); Eduardo Ulises Moya (Gobierno de Jalisco); Michael Smith (Aspia Space); Yazid Mikail (Climate Change AI); Daisy Ondwari (Kabarak University)
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 several downstream policy and research applications. This proposal presents 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.