A lightweight model based on YOLOv8n in wheat spike detection-Reference-Cited by-同舟云学术

A lightweight model based on YOLOv8n in wheat spike detection

Published:2023-07-25 Issue: Volume: Page:
ISSN:
Container-title:2023 11th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)
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Author:

Ban Xuyang¹,Liu Pan¹,Xu Lei¹,Zhao Jinling²

Affiliation:

1. Anhui University,School of Electronics and Information Engineering.,Hefei,China

2. Anhui University,Anhui Engineering Laboratory of Agro-Ecological Big Data.,Hefei,China

Funder

Natural Science Foundation of Anhui Province

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10233256/10233125/10233526.pdf?arnumber=10233526

Reference21 articles.

1. Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model

2. Automatic Detection and Counting of Wheat Spikelet Using Semi-Automatic Labeling and Deep Learning

3. Development of image-based wheat spike counter through a Faster R-CNN algorithm and application for genetic studies

4. SSRNet: In-field counting wheat ears using multi-stage convolutional neural network[J];wang;IEEE Transactions on Geoscience and Remote Sensing,2021

5. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[J];wang;ArXiv e-prints,2022

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