High-Precision Wheat Head Detection Model Based on One-Stage Network and GAN Model
Frontiers in Plant Science, ISSN: 1664-462X, Vol: 13, Page: 787852
2022
- 29Citations
- 12Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations29
- Citation Indexes29
- 29
- Captures12
- Readers12
- 12
Article Description
Counting wheat heads is a time-consuming process in agricultural production, which is currently primarily carried out by humans. Manually identifying wheat heads and statistically analyzing the findings has a rigorous requirement for the workforce and is prone to error. With the advancement of machine vision technology, computer vision detection algorithms have made wheat head detection and counting feasible. To accomplish this traditional labor-intensive task and tackle various tricky matters in wheat images, a high-precision wheat head detection model with strong generalizability was presented based on a one-stage network structure. The model's structure was referred to as that of the YOLO network; meanwhile, several modules were added and adjusted in the backbone network. The one-stage backbone network received an attention module and a feature fusion module, and the Loss function was improved. When compared to various other mainstream object detection networks, our model outperforms them, with a mAP of 0.688. In addition, an iOS-based intelligent wheat head counting mobile app was created, which could calculate the number of wheat heads in images shot in an agricultural environment in less than a second.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85132833948&origin=inward; http://dx.doi.org/10.3389/fpls.2022.787852; http://www.ncbi.nlm.nih.gov/pubmed/35720576; https://www.frontiersin.org/articles/10.3389/fpls.2022.787852/full; https://dx.doi.org/10.3389/fpls.2022.787852; https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2022.787852/full
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