@article{martinez_kudenov_dunne_garrity_2025, title={Enhancing peanut pre‐sizing with PodTracker: A multiple pod tracking algorithm}, volume={8}, url={https://doi.org/10.1002/ppj2.70035}, DOI={10.1002/ppj2.70035}, abstractNote={Abstract In this work, we introduce novel computer vision and machine learning methods—including instance segmentation (Mask R‐CNN [Region‐Based Convolutional Neural Network]), multiple object tracking (PodTracker and DeepSortMask), and classification (Decision Tree)—to improve peanut pod pre‐sizing at grading stations and evaluate the sizing error associated with conventional pre‐sizer mechanical rollers. The trained Mask R‐CNN model achieved high bounding box average precision (AP) scores, with on Farmer Stock Fancy pods and on No. 1 pods. PodTracker (, where RMSE is root mean square error) outperformed DeepSortMask () in counting and tracking pods across frames. The classification model achieved an F1 score across No. 1, Fancy, and Jumbo pods, exhibiting lower standard deviations () compared to the conventional mechanical pre‐sizer (). These results demonstrate that low‐cost scanning solutions, when combined with advanced computer vision techniques, can be effectively integrated into conventional pre‐sizing machinery to offer more accurate sizing distributions than traditional methods.}, number={1}, journal={The Plant Phenome Journal}, author={Martinez, Enrique E. Pena and Kudenov, Michael W. and Dunne, Jeff and Garrity, Nick}, year={2025}, month={Aug} }
 @article{newman_austin_andres_read_garrity_fritz_oakley_hulse‐kemp_dunne_2025, title={Evaluating UAV captured RGB and multispectral imagery as a proxy for visual rating of leaf spot in cultivated peanut}, volume={8}, DOI={10.1002/ppj2.70019}, abstractNote={Abstract Leaf spot is a devastating disease in cultivated peanut ( Arachis hypogaea L.) that can lead to significant yield losses without chemical controls. Multiple disease symptoms, two causal organisms, inconsistent testing environments, and genotype by environment interactions are all components that make breeding for leaf spot‐resistant peanuts challenging. To better understand this disease, and make gains in breeding for disease resistance, an accurate and objective phenotyping strategy must be implemented. In this work, data derived from leaf scans, unoccupied aerial vehicle‐captured red, green, blue and multispectral imagery were evaluated as a replacement for the subjective visual rating scale used at present. Standard operating procedures are detailed for all digital methods evaluated in this paper, and all digital phenotypes are fully characterized with descriptive statistics. Feature importance and post hoc proof of concept studies are conducted to further evaluate the new digital methods. Ultimately, “visible atmospherically resistant index” was selected as the most appropriate proxy for visual ratings and should be deployed by researchers and plant breeders in the peanut community for the objective evaluation of leaf spot resistance.}, number={1}, journal={The Plant Phenome Journal}, author={Newman, Cassondra and Austin, Robert and Andres, Ryan and Read, Quentin and Garrity, Nick and Fritz, Kaitlyn and Oakley, Andrew and Hulse‐Kemp, Amanda and Dunne, Jeffrey}, year={2025}, month={May} }