More Like this

1. Automated species-level identification of planktic foraminifera using convolutional neural networks, with comparison to human performance

   https://doi.org/10.1016/j.marmicro.2019.01.005  

   Mitra, R.; Marchitto, T.M.; Ge, Q.; Zhong, B.; Kanakiya, B.; Cook, M.S.; Fehrenbacher, J.S.; Ortiz, J.D.; Tripati, A.; Lobaton, E. (March 2019, Marine Micropaleontology)
2. Automatic Identification of Center Pivot Irrigation Systems from Landsat Images Using Convolutional Neural Networks

   https://doi.org/10.3390/agriculture8100147  

   Zhang, Chenxiao; Yue, Peng; Di, Liping; Wu, Zhaoyan (October 2018, Agriculture)

   Being hailed as the greatest mechanical innovation in agriculture since the replacement of draft animals by the tractor, center pivot irrigation systems irrigate crops with a significant reduction in both labor and water needs compared to traditional irrigation methods, such as flood irrigation. In the last few decades, the deployment of center pivot irrigation systems has increased dramatically throughout the United States. Monitoring the installment and operation of the center pivot systems can help: (i) Water resource management agencies to objectively assess water consumption and properly allocate water resources, (ii) Agro-businesses to locate potential customers, and (iii) Researchers to investigate land use change. However, few studies have been carried out on the automatic identification and location of center pivot irrigation systems from satellite images. Growing rapidly in recent years, machine learning techniques have been widely applied on image recognition, and they provide a possible solution for identification of center pivot systems. In this study, a Convolutional Neural Networks (CNNs) approach was proposed for identification of center pivot irrigation systems. CNNs with different structures were constructed and compared for the task. A sampling approach was presented for training data augmentation. The CNN with the best performance and less training time was used in the testing area. A variance-based approach was proposed to further locate the center of each center pivot system. The experiment was applied to a 30-m resolution Landsat image, covering an area of 20,000 km2 in North Colorado. A precision of 95.85% and a recall of 93.33% of the identification results indicated that the proposed approach performed well in the center pivot irrigation systems identification task. 

   more » « less
3. Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection

   https://doi.org/10.1038/s41598-021-92891-9  

   Goodwin, Adam; Padmanabhan, Sanket; Hira, Sanchit; Glancey, Margaret; Slinowsky, Monet; Immidisetti, Rakhil; Scavo, Laura; Brey, Jewell; Sai Sudhakar, Bala Murali; Ford, Tristan; et al (December 2021, Scientific Reports)

   Abstract With over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel species. Closed-set classification of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region. 

   more » « less
4. Decidual Vasculopathy Identification in Whole Slide Images Using Multiresolution Hierarchical Convolutional Neural Networks

   https://doi.org/10.1016/j.ajpath.2020.06.014  

   Clymer, Daniel; Kostadinov, Stefan; Catov, Janet; Skvarca, Lauren; Pantanowitz, Liron; Cagan, Jonathan; LeDuc, Philip (October 2020, The American Journal of Pathology)
5. Graph Convolutional Neural Networks for Power Line Outage Identification

   https://doi.org/10.1109/ICPR48806.2021.9413093  

   He, Jia; Cheng, Maggie (January 2021, IEEE ICPR 2020)