Semi-Supervised Object Detection for Sorghum Panicles in UAV Imagery

• URL: http://arxiv.org/abs/2305.09810v1
• Date: Tue, 16 May 2023 21:24:26 GMT
• Title: Semi-Supervised Object Detection for Sorghum Panicles in UAV Imagery
• Authors: Enyu Cai, Jiaqi Guo, Changye Yang, Edward J. Delp
• Abstract summary: sorghum panicle is an important trait related to grain yield and plant development. Current deep-learning-based object detection methods for panicles require a large amount of training data. We present an approach to reduce the amount of training data for sorghum panicle detection via semi-supervised learning.
• Score: 22.441677896192363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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