ArticleSensors (Basel, Switzerland)2024
Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Feature Selection Based on Information Entropy for Accurate Detection of Optical Fiber End-Face Defects.Entropy (Basel, Switzerland) · 2026Article
- Enhanced recognition and counting of high-coverage Amorphophallus konjac by integrating UAV RGB imagery and deep learning.Scientific reports · 2025Article
- A rapid and precise algorithm for maize leaf disease detection based on YOLO MSM.Scientific reports · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series 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 (
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Registered trials
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