Can AI See What We Can't? Leveraging Deep Learning and Multi-Temporal Satellite Data to Revolutionize Crop Type Mapping and Yield Prediction
Gautam Siddharth Kashyap, Harsh Joshi, Manaswi Kulahara, Rajkumar Dhakar, Atul Sajjanhar, Jiechao Gao, Sarthak Jain, Shahab Saquib Sohail
Abstract
Precise mapping of crop types and estimating yields are important in gauging agricultural diversity and yield potential, especially in regions dominated by small-scale farming. Nevertheless, these tasks are challenging due to factors such as small field sizes, inter-cropping, and a lack of sufficient ground truth labels for certain regions. In this paper, we propose an approach that combines advanced deep learning algorithms with Sentinel-2 and MODIS satellite data for improving the accuracy of crop type mapping and yield prediction. We used datasets from the main growing season of 2017 in Kenya (Bungoma, Busia and Siaya) coupled with county level yield data from US, Argentina and Brazil spanning from 2005 to 2016. Our models (CNN, SegNet, MaskRCNN, ResNet, UNet) were evaluated on both tasks i.e., classification of crop types and predicting yields.
BibTeX
@inproceedings{icassp2025_canaiseewhatweca,
title = {Can AI See What We Can't? Leveraging Deep Learning and Multi-Temporal Satellite Data to Revolutionize Crop Type Mapping and Yield Prediction},
author = {Gautam Siddharth Kashyap and Harsh Joshi and Manaswi Kulahara and Rajkumar Dhakar and Atul Sajjanhar and Jiechao Gao and Sarthak Jain and Shahab Saquib Sohail},
booktitle = {ICASSP 2025},
year = {2025}
}