AgriNema

AgriNema数据集是由谢菲尔德大学计算机科学系创建的，专门用于农业中植物寄生线虫检测。该数据集包含525张显微镜图像，涵盖了Meloidogyne hapla、Globodera pallida等多种线虫种类。创建过程涉及从显微镜中收集图像，并对其进行标注以供深度学习模型训练使用。该数据集主要应用于农业领域，旨在通过图像分析技术提高线虫检测的效率和准确性，从而减少作物损失。

The AgriNema dataset was developed by the Department of Computer Science at the University of Sheffield, specifically for the detection of plant-parasitic nematodes in agriculture. This dataset consists of 525 microscopic images, covering multiple nematode species including *Meloidogyne hapla* and *Globodera pallida*. The dataset creation process involves collecting images from microscopes and annotating them for training deep learning models. Primarily applied in the agricultural sector, this dataset aims to improve the efficiency and accuracy of nematode detection via image analysis technologies, thereby reducing crop losses.