Unverified paper record
Semi-Supervised Object Detection for Sorghum Panicles in UAV Imagery
arXiv (Cornell University) · 16 May 2023 · 10.48550/arxiv.2305.09810
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.
Plant phenotyping relevance
ソルガム穂の検出・計数を植物表現型として取得する画像解析手法を、半教師あり学習で開発・評価しており、表現型取得法が中心である。
abstractDetecting and counting sorghum panicles can provide significant information for plant phenotyping.
abstractIn this paper, we present an approach to reduce the amount of training data for sorghum panicle detection via semi-supervised learning.
abstractResults show we can achieve similar performance as supervised methods for sorghum panicle detection by only using 10\% of original training data.
Code and data availability
The paper describes a semi-supervised sorghum panicle detection method using UAV imagery, but no public dataset, code, model, or supplement with an authors' URL or deposit is mentioned. The imagery was provided by Purdue's DPRG with no availability statement, and no code release is described.
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