Biological Systems Engineering, Department of
Document Type
Article
Date of this Version
3-28-2024
Citation
Rodene, E.; Fernando, G.D.; Piyush, V.; Ge, Y.; Schnable, J.C.; Ghosh, S.; Yang, J. Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms. Sensors 2024, 24, 2172. https://doi.org/10.3390/s24072172
Abstract
Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect timeseries agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy (r2) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement.
Included in
Bioresource and Agricultural Engineering Commons, Environmental Engineering Commons, Other Civil and Environmental Engineering Commons
Comments
Open access.