用于可可植物 CSSVD 检测的图像分类
Image Classification for CSSVD Detection in Cacao Plants
AI总结：
针对可可 CSSVD 标注数据稀缺问题，本文基于 VGG16、ResNet50 和 ViT 构建检测分类器，并在 KaraAgroAI Cocoa 数据集上验证，ResNet50 取得 94% 准确率且召回率提升 9.75%。
摘要：
植物内部病害的检测已经吸引了计算机视觉爱好者的大量关注。尽管在检测许多植物病害方面已经取得进展，但在训练图像分类器以检测与可可植物相关的可可肿枝病毒病（简称 CSSVD）方面仍存在研究空白。这一空白主要源于缺乏高质量的标注训练数据。此外，各机构一直不愿分享其与 CSSVD 相关的数据。为填补这些空白，我们提出开发用于检测感染 CSSVD 的可可植物的图像分类器。我们提出的解决方案基于 VGG16、ResNet50 和 Vision Transformer（ViT）。我们在最近发布且可公开获取的 KaraAgroAI Cocoa 数据集上评估这些分类器。我们的最佳图像分类器基于 ResNet50，仅训练 20 个 epoch 即达到 95.39% 精确率、93.75% 召回率、94.34% F1 分数和 94% 准确率。与以往工作相比，召回率提升了 9.75%。我们的结果表明，这些图像分类器能够学会识别感染 CSSVD 的可可植物。
英文摘要：
The detection of diseases within plants has attracted a lot of attention from computer vision enthusiasts. Despite the progress made to detect diseases in many plants, there remains a research gap to train image classifiers to detect the cacao swollen shoot virus disease or CSSVD for short, pertinent to cacao plants. This gap has mainly been due to the unavailability of high quality labeled training data. Moreover, institutions have been hesitant to share their data related to CSSVD. To fill these gaps, we propose the development of image classifiers to detect CSSVD-infected cacao plants. Our proposed solution is based on VGG16, ResNet50 and Vision Transformer (ViT). We evaluate the classifiers on a recently released and publicly accessible KaraAgroAI Cocoa dataset. Our best image classifier, based on ResNet50, achieves 95.39\% precision, 93.75\% recall, 94.34\% F1-score and 94\% accuracy on only 20 epochs. There is a +9.75\% improvement in recall when compared to previous works. Our results indicate that the image classifiers learn to identify cacao plants infected with CSSVD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
