Convolutional neural network-based automatic root length measurement in noisy rhizosphere images

Abstract

<p>The use of minirhizotron (MR) imaging systems is gaining popularity, resulting in a large amount of collected images—which need efficient and accurate processing for root trait extraction. This study proposes a neural network-based solution for automatic measurement of root length in images taken by MR systems. Current root length measurement techniques involve two steps; manually operating the MR for taking the images, and manually annotating roots in front of a noisy rhizosphere ‘background’ with a dedicated software. As the analysing process is extremely time consuming, automation can both lower the costs and facilitate greater temporal resolution.</p><p>Using convolutional neural networks (CNN) in image classification tasks has become very common due to its simplicity, yet regression tasks are still considered difficult. We propose a new model that combines the strength of conditional learning, transfer learning and bagging in order to achieve a precise regression. The dataset used holds 12,000 highly diverse images of 5 tomatoes cultivars, which were collected by a BARTZ minirhizotron camera over a period of 4 months.</p><p>Initial results show a success rate of 75% accuracy with 33 mm Mean Absolute Error (MAE). Error analysis shows that large errors occur on images with either a very high or a low root length density. Additionally, a separate model was designed and tested on selected subsets of the data by using a synthetic data generator. Results show that MAE decreases to 10 mm, which is equivalent to 90% accuracy.</p><p>Results suggest that this method has great potential to facilitate fully automatic root length measurement on noisy rhizosphere images. Future work will validate the proposed model with a larger datasets comprising of various plant species, soil types and MR imaging systems.</p>