2023
DOI: 10.3390/rs15184562
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Improving the Transferability of Deep Learning Models for Crop Yield Prediction: A Partial Domain Adaptation Approach

Yuchi Ma,
Zhengwei Yang,
Qunying Huang
et al.

Abstract: Over the past few years, there has been extensive exploration of machine learning (ML), especially deep learning (DL), for crop yield prediction, resulting in impressive levels of accuracy. However, such models are highly dependent on training samples with ground truth labels (i.e., crop yield records), which are not available in some regions. Additionally, due to the existence of domain shifts between different spatial regions, DL models trained within one region (i.e., source domain) tend to have poor perfor… Show more

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Cited by 7 publications
(1 citation statement)
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“…Hilal et al [26] extend the idea of a pretrained ResNet50 for land cover classification by including discrete local binary patterns to the ResNet features for the final classification. In very recent advances, Ma et al [27] improve the idea of Domain-Adversial Neural Networks (DANNs) [28], where transfer learning is extended by projecting the input features of each domain into a common subspace. Their proposed partial DANN (PDANN) applies weights to the source samples according to their estimated yield distribution in the target domain and is used to improve the transfer learning of soybean and corn yields between different regions in the US.…”
Section: Transfer Learning For Remote Sensing Applicationsmentioning
confidence: 99%
“…Hilal et al [26] extend the idea of a pretrained ResNet50 for land cover classification by including discrete local binary patterns to the ResNet features for the final classification. In very recent advances, Ma et al [27] improve the idea of Domain-Adversial Neural Networks (DANNs) [28], where transfer learning is extended by projecting the input features of each domain into a common subspace. Their proposed partial DANN (PDANN) applies weights to the source samples according to their estimated yield distribution in the target domain and is used to improve the transfer learning of soybean and corn yields between different regions in the US.…”
Section: Transfer Learning For Remote Sensing Applicationsmentioning
confidence: 99%