Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images

作者：John Atanbori, Maria Elker Montoya-P, Michael Gomez Selvaraj, Andrew P. French, Tony Pridmore · 发表于：Frontiers in Plant Science · 年份：2019 · DOI：10.3389/fpls.2019.01516 · 被引用次数：21 · 研究领域：Smart Agriculture and AI、Cassava research and cyanide、Plant responses to water stress

Cassava roots are complex structures comprising several distinct types of root. The number and size of the storage roots are two potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots are usually done manually, or semi-automatically by first segmenting cassava root images. However, occlusion of both storage and fibrous roots makes the process both time-consuming and error-prone. While Convolutional Neural Nets have shown performance above the state-of-the-art in many image processing and analysis tasks, there are currently a limited number of Convolutional Neural Net-based methods for counting plant features. This is due to the limited availability of data, annotated by expert plant biologists, which represents all possible measurement outcomes. Existing works in this area either learn a direct image-to-count regressor model by regressing to a count value, or perform a count after segmenting the image. We, however, address the problem using a direct image-to-count prediction model. This is made possible by generating synthetic images, using a conditional Generative Adversarial Network (GAN), to provide training data for missing classes. We automatically form cassava storage root masks for any missing classes using existing ground-truth masks, and input them as a condition to our GAN model to generate synthetic root images. We combine the resulting synthetic images with real images to learn a direct image-to-coun...