A synthetic datasets based instance segmentation network for High-throughput soybean pods phenotype investigation

• Designed an Expert System for high throughput soybean pods phenotype investigation. • A novel method for synthesizing training datasets automatically. • A hybrid sim/real dataset designed for soybean pods instance segmentation. • Significant improvement on accuracy of phenotype by segmented-mask based method. The segmentation of individual soybean pod is the prerequisite step for obtaining phenotypic traits such as pod length, width, and the number of seeds per pod. Nevertheless, handcrafted features-based image methods are not robust and practical in segmenting soybean pods with several morphological peculiarities in varying degrees. Although deep learning-based algorithms can achieve accurate training and strong generalization capabilities, it requires massive hand-labeled datasets, especially in the agricultural realm. Hence, we present a novel image synthesis method for rapidly generating a high throughput soybean pods dataset with plenty of raw images and mask images, where the soybean pods are densely sampled for simulating frequently physically touching. Then, we train a variant of Mask R-CNN on our synthetic dataset without any manual-labeled data and evaluate the trained model on our synthetic test images dataset and a real-world test image dataset of densely-cluttered soybean pods with Average Precision and Average Recall. Finally, a new segmented mask-based method is proposed for calculating the pod length and width and validation is carried out. The experimental results show that the proposed expert system could be used to quickly and efficiently segment and calculate the morphological parameters of each pod and it is practical to use this approach for high-throughput object instance segmentation and measurement.