Soybean root image dataset and its deep learning application for nodule segmentation

The root is very important for anchoring the plant and acquiring water and nutrients from the soil for growth. Plant root trait phenotyping using imaging (2D and 3D) techniques is gaining importance in agriculture to enhance the breeding of superior cultivars. However, most root image data have been collected under controlled growth conditions, such as in greenhouses and growth chambers. In this study, we propose collecting soybean root image datasets from open field conditions for root phenotyping. There are numerous methods for segmenting images, but many of them are not suitable for the tiny and nodulated architecture of root. Advancements such as deep learning (DL) methods and semantic segmentation algorithms have been actively applied in many research studies. In the current study, we used convolutional neural network (CNN) based U-Net with six convolution methods and Deeplabv3+ and compared their performance in nodule identification based on the Dice coefficient, GPU memory usage, qualitative image segmentation, training time, and inference time. Also, we employed two image processing, resizing and patching, for efficient training of images with very high resolution. The results indicated accurate segmentation of root nodules; in addition, the results for the segmentation of root nodules using DL high-resolution (HR) images were more precise and efficient than those with low-resolution (LR) images since the Dice coefficient value and rate of nodule size were high in HR images although the former required more training time and higher GPU memory. Furthermore, in comparison to the resizing approach, patch-based approach outperformed in all aspects of performance. Among the DL models that we employed for our study, the grouped convolution-based U-net showed the best results having high Dice coefficient values 0.647 and 0.618 for 600x400 and 300x200 images respectively in resizing approach and dynamic convolution-based U-net showed the best results having Dice coefficient values 0.792 and 0.777 for 600x400 and 300x200 patch images respectively in patch-based approach. Thus, the optimized DL model presented in this study has the potential to be a valuable tool for the automated analysis of soybean root systems, assisting in the measurement of various root quality parameters and contributing to the development of superior cultivars.