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Abstract—We consider the ability of CLIP features to support text-driven image retrieval. Traditional image-based queries sometimes misalign with user intentions due to their focus on irrelevant image components. To overcome this, we explore the potential of text-based image retrieval, specifically using Contrastive Language-Image Pretraining (CLIP) models. CLIP models, trained on large datasets of image-caption pairs, offer a promising approach by allowing natural language descriptions for more targeted queries. We explore the effectiveness of textdriven image retrieval based on CLIP features by evaluating the image similarity for progressively more detailed queries. We find that there is a sweet-spot of detail in the text that gives best results and find that words describing the “tone” of a scene (such as messy, dingy) are quite important in maximizing text-image similarity. 

We explore the use of deep convolutional neural networks (CNNs) trained on overhead imagery of biomass sorghum to ascertain the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. We consider both CNNs trained explicitly on the classification task of predicting whether an image shows a plant with a reference or alternate version of various SNPs as well as CNNs trained to create data-driven features based on learning features so that images from the same plot are more similar than images from different plots, and then using the features this network learns for genetic marker classification. We characterize how efficient both approaches are at predicting the presence or absence of a genetic markers, and visualize what parts of the images are most important for those predictions. We find that the data-driven approaches give somewhat higher prediction performance, but have visualizations that are harder to interpret; and we give suggestions of potential future machine learning research and discuss the possibilities of using this approach to uncover unknown genotype × phenotype relationships.