ArticlePlants (Basel, Switzerland)2023
Segmentation and Phenotype Calculation of Rapeseed Pods Based on YOLO v8 and Mask R-Convolution Neural Networks.
Article in Plants (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Comparative Evaluation of YOLOv8 and YOLOv11 for Digital Phenotyping of Edible Mushrooms Under Controlled Cultivation Conditions.Journal of fungi (Basel, Switzerland) · 2026Article
- Deep learning-based high-throughput phenotyping for tiller quantification in interspecific bentgrass hybrids using YOLOv8.Frontiers in plant science · 2026Article
- APTES: a high-throughput deep learning-based Arabidopsis phenotypic trait estimation system for individual leaves and siliques.aBIOTECH · 2025Article
- Stoma Detection in Soybean Leaves and Rust Resistance Analysis.Plants (Basel, Switzerland) · 2025Article
- Deep learning-based automatic detection and grading of disk herniation in lumbar magnetic resonance images.Scientific reports · 2025Article
- Deep Learning Models for Detection and Severity Assessment of Cercospora Leaf Spot (Plants (Basel, Switzerland) · 2025Article
- PodNet: Pod real-time instance segmentation in pre-harvest soybean fields.Plant phenomics (Washington, D.C.) · 2025Article
- Comparison of mask R-CNN and YOLOv8-seg for improved monitoring of the PCB surface during laser cleaning.Scientific reports · 2025Article
- YOLO-ODD: an improved YOLOv8s model for onion foliar disease detection.Frontiers in plant science · 2025Article
- Exploiting 2D Neural Network Frameworks for 3D Segmentation Through Depth Map Analytics of Harvested Wild Blueberries (Journal of imaging · 2024Article
- Enhancing cotton whitefly (Bemisia tabaci) detection and counting with a cost-effective deep learning approach on the Raspberry Pi.Plant methods · 2024Article
- Rapeseed Flower Counting Method Based on GhP2-YOLO and StrongSORT Algorithm.Plants (Basel, Switzerland) · 2024Article
- Dense object detection methods in RAW UAV imagery based on YOLOv8.Scientific reports · 2024Article
- Deep learning-based elaiosome detection in milk thistle seed for efficient high-throughput phenotyping.Frontiers in plant science · 2024Article
- YOLOv8-segANDcal: segmentation, extraction, and calculation of soybean radicle features.Frontiers in plant science · 2024Article
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5 authors.
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Abstract
Rapeseed is a significant oil crop, and the size and length of its pods affect its productivity. However, manually counting the number of rapeseed pods and measuring the length, width, and area of the pod takes time and effort, especially when there are hundreds of rapeseed resources to be assessed. This work created two state-of-the-art deep learning-based methods to identify rapeseed pods and related pod attributes, which are then implemented in rapeseed pots to improve the accuracy of the rapeseed yield estimate. One of these methods is YOLO v8, and the other is the two-stage model Mask R-CNN based on the framework Detectron2. The YOLO v8n model and the Mask R-CNN model with a Resnet101 backbone in Detectron2 both achieve precision rates exceeding 90%. The recognition results demonstrated that both models perform well when graphic images of rapeseed pods are segmented. In light of this, we developed a coin-based approach for estimating the size of rapeseed pods and tested it on a test dataset made up of nine different species of
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