Spatio-temporal Mapping of Cotton Blooms Appearance Using Deep Learning
Year: 2022
Type: article
Abstract: In this paper, we present an approach to create spatio-temporal maps using deep learning to visualize cotton bloom appearance over time. Specifically, we manually annotate cotton flower image data and train three state-of-the-art fast deep neural network models to count cotton blooms and their frequency over time prior to harvesting. We use the detection results of the best performing model combined with traditional pixel-based image analysis methods to create a map of where past and future blooms grow on a mid-stage cotton plant. The training results of our best model show a visual understanding of how many cotton flowers grow with high F1 Score of more than 0.95, a true positive rate of 98%, false negative and false positive rates both under 10%, and millisecond-scale inference time for real-time processing.
Source: IFAC-PapersOnLine
Institution: University of Georgia
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4312369911