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Advancing Tassel Detection and Counting: Annotation and Algorithms
- Source :
- Remote Sensing, Vol 13, Iss 2881, p 2881 (2021), Remote Sensing; Volume 13; Issue 15; Pages: 2881
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- Tassel counts provide valuable information related to flowering and yield prediction in maize, but are expensive and time-consuming to acquire via traditional manual approaches. High-resolution RGB imagery acquired by unmanned aerial vehicles (UAVs), coupled with advanced machine learning approaches, including deep learning (DL), provides a new capability for monitoring flowering. In this article, three state-of-the-art DL techniques, CenterNet based on point annotation, task-aware spatial disentanglement (TSD), and detecting objects with recursive feature pyramids and switchable atrous convolution (DetectoRS) based on bounding box annotation, are modified to improve their performance for this application and evaluated for tassel detection relative to Tasselnetv2+. The dataset for the experiments is comprised of RGB images of maize tassels from plant breeding experiments, which vary in size, complexity, and overlap. Results show that the point annotations are more accurate and simpler to acquire than the bounding boxes, and bounding box-based approaches are more sensitive to the size of the bounding boxes and background than point-based approaches. Overall, CenterNet has high accuracy in comparison to the other techniques, but DetectoRS can better detect early-stage tassels. The results for these experiments were more robust than Tasselnetv2+, which is sensitive to the number of tassels in the image.
- Subjects :
- 0106 biological sciences
Computer science
Science
Tassel
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
maize
01 natural sciences
Convolution
Bounding overwatch
Minimum bounding box
tassel detection
0202 electrical engineering, electronic engineering, information engineering
Point (geometry)
business.industry
Deep learning
deep learning
Pattern recognition
annotation
Feature (computer vision)
General Earth and Planetary Sciences
RGB color model
020201 artificial intelligence & image processing
Artificial intelligence
business
010606 plant biology & botany
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 13
- Issue :
- 2881
- Database :
- OpenAIRE
- Journal :
- Remote Sensing
- Accession number :
- edsair.doi.dedup.....6cce1ff7275697ceeca85bcd0a409065