A vision transformer that fuses satellite imagery, climate data, and management text predicts vineyard yields with R2=0.84 on held-out blocks, outperforming a UNet-ConvLSTM baseline.
Efficient identification, localization and quantification of grapevine inflorescences and flowers in unprepared field images using fully convolutional networks
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CMAViT: Integrating Climate, Managment, and Remote Sensing Data for Crop Yield Estimation with Multimodel Vision Transformers
A vision transformer that fuses satellite imagery, climate data, and management text predicts vineyard yields with R2=0.84 on held-out blocks, outperforming a UNet-ConvLSTM baseline.