An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection
This addresses a critical plant science issue causing $1 billion in annual losses in the U.S. by automating egg detection, but it is incremental as it builds on existing deep learning methods for a specific domain.
The paper tackles the problem of detecting Soybean Cyst Nematode eggs in microscopic images, which is challenging due to similar non-egg particles, and proposes a selective autoencoder approach that achieves automated high-throughput detection to expedite the labor-intensive manual process.
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.