Original Research ARTICLE
CNN-Based Cassava Storage Root Counting using Real and Synthetic Images
- 1University of Nottingham, United Kingdom
- 2International Center for Tropical Agriculture (CIAT), Colombia
- 3Biosciences, University of Nottingham, United Kingdom
- 4School of Computer Science, Faculty of Science, University of Nottingham, United Kingdom
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 (CNNs) have shown performance above the state-of-the art in many image processing and analysis tasks, there are currently a limited number of CNN-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-count prediction model capable of counting the number of storage roots in real cassava images taken from a low cost aeroponic growth system}. These models are used to develop a system that counts cassava storage roots in real images. Our system first predicts age group ('young' and 'old' roots; pertinent to our image capture regime) in a given image, and then, based on this prediction, selects an appropriate model to predict the number of storage roots. We achieve 91% accuracy on predicting ages of storage roots, and 86% and 71% overall percentage agreement on counting 'old' and 'young' storage roots respectively. Thus we are able to demonstrate that synthetically generated cassava root images can be used to supplement missing root classes, turning the counting problem into a direct image-to-count prediction task.
Keywords: Machine leaming, Plant phenotyping tool, Cassava (Manhiot esculenta), Software Engineering, Deep learning (DL) approaches
Received: 05 Jul 2019;
Accepted: 31 Oct 2019.
Copyright: © 2019 Atanbori, Montoya, Selvaraj, French and Pridmore. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Prof. Tony P. Pridmore, University of Nottingham, Nottingham, NG7 2RD, East Midlands, United Kingdom, firstname.lastname@example.org