ArticleSensors (Basel, Switzerland)2021
Wheat Ear Recognition Based on RetinaNet and Transfer Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- High-throughput phenotyping of wheat ear surface area and ear density in the field.Plant phenomics (Washington, D.C.) · 2026Article
- Transfer learning models for wheat ear detection on multi-source dataset.Scientific reports · 2025Article
- CropCLR-Wheat: A Label-Efficient Contrastive Learning Architecture for Lightweight Wheat Pest Detection.Insects · 2025Article
- RGB imaging and computer vision-based approaches for identifying spike number loci for wheat.Plant phenomics (Washington, D.C.) · 2025Article
- Research on temperature detection method of liquor distilling pot feeding operation based on a compressed algorithm.Scientific reports · 2024Article
- Improved YOLO-FastestV2 wheat spike detection model based on a multi-stage attention mechanism with a LightFPN detection head.Frontiers in plant science · 2024Article
- WheatLFANet: in-field detection and counting of wheat heads with high-real-time global regression network.Plant methods · 2023Article
- Segmentation and counting of wheat spike grains based on deep learning and textural feature.Plant methods · 2023Article
- Research on the Method of Counting Wheat Ears via Video Based on Improved YOLOv7 and DeepSort.Sensors (Basel, Switzerland) · 2023Article
- Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet.Plant phenomics (Washington, D.C.) · 2023Article
- A lightweight network for improving wheat ears detection and counting based on YOLOv5s.Frontiers in plant science · 2023Article
- Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting.Sensors (Basel, Switzerland) · 2022Article
- Advances in Deep-Learning-Based Sensing, Imaging, and Video Processing.Sensors (Basel, Switzerland) · 2022Article
- Rapid Detection of Wheat Ears in Orthophotos From Unmanned Aerial Vehicles in Fields Based on YOLOX.Frontiers in plant science · 2022Article
- Lightweight and efficient neural network with SPSA attention for wheat ear detection.PeerJ. Computer science · 2022Article
- Wheat Spike Detection and Counting in the Field Based on SpikeRetinaNet.Frontiers in plant science · 2022Article
- Detection method of wheat spike improved YOLOv5s based on the attention mechanism.Frontiers in plant science · 2022Article
- A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images.Frontiers in plant science · 2022Article
- Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods.Plant phenomics (Washington, D.C.) · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
The number of wheat ears is an essential indicator for wheat production and yield estimation, but accurately obtaining wheat ears requires expensive manual cost and labor time. Meanwhile, the characteristics of wheat ears provide less information, and the color is consistent with the background, which can be challenging to obtain the number of wheat ears required. In this paper, the performance of Faster regions with convolutional neural networks (Faster R-CNN) and RetinaNet to predict the number of wheat ears for wheat at different growth stages under different conditions is investigated. The results show that using the Global WHEAT dataset for recognition, the RetinaNet method, and the Faster R-CNN method achieve an average accuracy of 0.82 and 0.72, with the RetinaNet method obtaining the highest recognition accuracy. Secondly, using the collected image data for recognition, the
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