2016/03/25 by Adedotun Akintayo, Nigel Lee, Akintayo, Adedotun +16
Agricultural and Biological Sciences · Computer Science · Mathematics · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Nematode management and characterization studies #Smart Agriculture and AI #Soybean genetics and cultivation #cs.CV #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1603.07834
A 10 pages, 8 figures International Conference on Machine Leaning(ICML) Submission
openalex publication_date 2016/03/25 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks. The crux of the idea is to train a deep convolutional autoencoder to suppress undesired parts of an image frame while allowing the desired parts resulting in efficient object detection. The efficacy of the framework is demonstrated on a critical plant science problem. In the United States, approximately 1 billion is lost per annum due to a nematode infection on soybean plants. Currently, plant-pathologists rely on labor-intensive and time-consuming identification of Soybean Cyst Nematode (SCN) eggs in soil samples via manual microscopy. The proposed framework attempts to significantly expedite the process by using a series of manually labeled microscopic images for training followed by automated high-throughput egg detection. The problem is particularly difficult due to the presence of a large population of non-egg particles (disturbances) in the image frames that are very similar to SCN eggs in shape, pose and illumination. Therefore, the selective autoencoder is trained to learn unique features related to the invariant shapes and sizes of the SCN eggs without handcrafting. After that, a composite non-maximum suppression and differencing is applied at the post-processing stage.