Transfer Learning for Maize Yield Prediction from Satellite Imagery

Joko Prasetyo Universitas Brawijaya
Maya Sari IPB University
Diterbitkan: 2025-12-15 Vol 1 No 2 (2025): Machine Learning for Agriculture Research Articles

Abstrak

We fine-tune a vision transformer on Sentinel-2 imagery to predict maize yield across Java, achieving an R2 of 0.87 and outperforming classical regression baselines by 21%.

Kata Kunci

  • transfer learning
  • remote sensing
  • maize
  • yield prediction

Referensi

  1. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
  2. Mohanty, S. P., Hughes, D. P., & Salathe, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419.
  3. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proc. IEEE CVPR (pp. 770-778).
  4. Howard, A. G., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861.
  5. Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In Proc. ICLR.
  6. Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In Proc. ICLR.
  7. Deng, J., et al. (2009). ImageNet: A large-scale hierarchical image database. In Proc. IEEE CVPR (pp. 248-255).
  8. Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311-318.
  9. Tan, M., & Le, Q. (2019). EfficientNet: Rethinking model scaling for CNNs. In Proc. ICML (pp. 6105-6114).
  10. Sandler, M., et al. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. In Proc. IEEE CVPR (pp. 4510-4520).
  11. Kamilaris, A., & Prenafeta-Boldu, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90.
  12. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

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