ArticleScientific reports2024
GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Multimodal-multiscale hybrid fusion network for automated esophageal cancer diagnosis.iScience · 2026Article
- A deep learning approach to county-level soybean yield forecasting using large-scale environmental data.Frontiers in artificial intelligence · 2026Article
- Sensor-Centric Intelligent Systems for Soybean Harvest Mechanization in Challenging Agro-Environments of China: A Review.Sensors (Basel, Switzerland) · 2025Review
- Dynamic gating-enhanced deep learning model with multi-source remote sensing synergy for optimizing wheat yield estimation.Frontiers in plant science · 2025Article
- Innovative food supply chain through spatial computing technologies: A review.Comprehensive reviews in food science and food safety · 2024Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Accurately estimating large-area crop yields, especially for soybeans, is essential for addressing global food security challenges. This study introduces a deep learning framework that focuses on precise county-level soybean yield estimation in the United States. It utilizes a wide range of multi-variable remote sensing data. The model used in this study is a state-of-the-art CNN-BiGRU model, which is enhanced by the GOA and a novel attention mechanism (GCBA). This model excels in handling intricate time series and diverse remote sensing datasets. Compared to five leading machine learning and deep learning models, our GCBA model demonstrates superior performance, particularly in the 2019 and 2020 evaluations, achieving remarkable R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.