# Semi-Supervised Object Detection for Sorghum Panicles in UAV Imagery

Source: [Semi-Supervised Object Detection for Sorghum Panicles in UAV Imagery](<https://www.sota2.com/research/paper/68fa6f511e209131cea44fca>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:58.783Z

Authors: Enyu Cai, Jiaqi Guo, Changye Yang, Edward J. Delp

Published: 2023-05-16T21:24:26.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.09810>)

[PDF](<https://arxiv.org/pdf/2305.09810.pdf>)