ArticleSensors (Basel, Switzerland)2023
An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery.
Article in Sensors (Basel, Switzerland), 2023. 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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3 citing papers in PubMed.
- Integrating UAV remote sensing and semi-supervised learning for early-stage maize seedling monitoring and geolocation.Plant phenomics (Washington, D.C.) · 2025Article
- MS-YOLOv8: multi-scale adaptive recognition and counting model for peanut seedlings under salt-alkali stress from remote sensing.Frontiers in plant science · 2024Article
- Weed detection in soybean fields using improved YOLOv7 and evaluating herbicide reduction efficacy.Frontiers in plant science · 2023Article
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2 authors.
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Abstract
Efficient detection and evaluation of soybean seedling emergence is an important measure for making field management decisions. However, there are many indicators related to emergence, and using multiple models to detect them separately makes data processing too slow to aid timely field management. In this study, we aimed to integrate several deep learning and image processing methods to build a model to evaluate multiple soybean seedling emergence information. An unmanned aerial vehicle (UAV) was used to acquire soybean seedling RGB images at emergence (VE), cotyledon (VC), and first node (V1) stages. The number of soybean seedlings that emerged was obtained by the seedling emergence detection module, and image datasets were constructed using the seedling automatic cutting module. The improved AlexNet was used as the backbone network of the growth stage discrimination module. The above modules were combined to calculate the emergence proportion in each stage and determine soybean seedlings emergence uniformity. The results show that the seedling emergence detection module was able to identify the number of soybean seedlings with an average accuracy of 99.92%, a R
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