SEMI-SUPERVISED OBJECT DETECTION FOR SORGHUM PANICLES IN UAV IMAGERY

被引：1

作者：

Cai, Enyu ^{[1
]}

Guo, Jiaqi ^{[1
]}

Yang, Changye ^{[1
]}

Delp, Edward J. ^{[1
]}

机构：

[1] Purdue Univ, Sch Elect & Comp Engn, Video & Image Proc Lab VIPER, W Lafayette, IN 47907 USA

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

关键词：

semi-supervised learning; plant phenotyping; panicle detection; sorghum;

D O I：

10.1109/IGARSS52108.2023.10282281

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

The sorghum panicle is an important trait related to grain yield and plant development. Detecting and counting sorghum panicles can provide significant information for plant phenotyping. Current deep-learning-based object detection methods for panicles require a large amount of training data. The data labeling is time-consuming and not feasible for real application. In this paper, we present an approach to reduce the amount of training data for sorghum panicle detection via semi-supervised learning. Results show we can achieve similar performance as supervised methods for sorghum panicle detection by only using 10% of original training data.

引用

页码：6482 / 6485

页数：4

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