High-Resolution UAV Image Generation for Sorghum Panicle Detection
Enyu Cai, Zhankun Luo, Sriram Baireddy, Jiaqi Guo, Changye Yang,, Edward J. Delp

TL;DR
This paper introduces a novel data augmentation method using GAN-generated synthetic UAV images to improve Sorghum panicle detection and counting, addressing the challenge of limited labeled data.
Contribution
It presents a GAN-based approach to generate synthetic high-resolution UAV images with labels, enhancing deep learning performance for plant phenotyping.
Findings
Improved panicle detection accuracy with synthetic data
Enhanced counting performance in UAV image analysis
Demonstrated effectiveness of GAN-based augmentation
Abstract
The number of panicles (or heads) of Sorghum plants is an important phenotypic trait for plant development and grain yield estimation. The use of Unmanned Aerial Vehicles (UAVs) enables the capability of collecting and analyzing Sorghum images on a large scale. Deep learning can provide methods for estimating phenotypic traits from UAV images but requires a large amount of labeled data. The lack of training data due to the labor-intensive ground truthing of UAV images causes a major bottleneck in developing methods for Sorghum panicle detection and counting. In this paper, we present an approach that uses synthetic training images from generative adversarial networks (GANs) for data augmentation to enhance the performance of Sorghum panicle detection and counting. Our method can generate synthetic high-resolution UAV RGB images with panicle labels by using image-to-image translation…
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Taxonomy
TopicsSmart Agriculture and AI · Remote Sensing in Agriculture · Remote Sensing and LiDAR Applications
