Deep Learning Driven, Image-Based Phenotyping of Seed Processing Efficiency in Sainfoin (Onobrychis viciifolia)

Bo Meyering^*Spencer BarriballBrandon Schlautman^*

• The Land Institute, Salina, United States

Sainfoin (Onobrychis spp.) is a perennial legume traditionally cultivated as a forage crop and is now emerging as a promising candidate for development as a perennial grain legume. However, no research has focused on understanding and breeding varieties with superior grain processing properties. In this study, we conducted a multifactorial experiment to depod and dehull seeds from five commercially available sainfoin varieties using two different methods and five sample sizes. We then fine-tuned a pre-trained a Faster R-CNN (Region-based Convolutional Neural Network) object detection model to detect intact pods, whole seeds, and split seeds from images of the processed seed mixtures and used these predictions to calculate the processing efficiency (PE) for each variety. We performed a comprehensive power analysis to determine the minimum sample size of sainfoin fruit pods required to detect differences in PE between breeding lines with high statistical power. Our results revealed strong varietal differences in PE, as well as clear effects attributed to the processing method. Samples processed with a belt thresher contained more intact pods, whereas the impact dehuller produced a higher proportion of split seeds. Larger sample sizes led to more intact pods in the mixture across all varieties and methods, and notably decreased the seed proportion in belt-threshed samples. Combining statistical modeling with seed count data from the Faster R-CNN predictions, we found that a minimum of 2g of seed is needed to reliably detect an absolute proportional difference of 0.25 between two breeding lines' PE with 80% power. Our findings emphasize that while deep learning models provide a powerful and cost-effective tool for phenotyping in plant breeding, their outputs must also be integrated with robust statistical design and methodology to yield reliable and actionable insights.