印度大豆数据集用于使用计算机视觉算法识别和分类疾病
Jameer Kotwal1, Ramgopal Kashyap1, Mohd Shafi Pathan2
1Amity University Chhattisgarh, 493225, India.
Data in brief
|March 7, 2024
概括
这项研究引入了一套新的大豆植物图像数据集,用于识别农业疾病. 该数据集有助于研究人员开发用于智能农业和作物健康监测的机器学习模型.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 智能农业越来越依赖于精确的疾病图像识别.
- 大豆是印度马哈拉施特拉州的一个重要作物,种植面积约为31,050公.
- 有效的疾病鉴定对于保持作物产量和质量至关重要.
研究的目的:
- 创建和发布一个全面的数据集健康和生病的大豆植物的叶片图像.
- 支持农业疾病图像识别和机器学习模型开发方面的研究.
- 为研究人员和学生提供可访问的数据,用于学术和实际应用.
主要方法:
- 从多个农场收集了两到三个季节的3363张大豆植物图像.
- 将图像分为六个类别:健康的植物,静脉亡,干叶,Septoria棕色斑点,根图像和细菌叶病.
- 为了方便访问,将数据集组织成七个不同的文件.
主要成果:
- 建立了3363张图像的多样化数据集,这些图像显示了大豆植物的各种条件.
- 数据集包括代表健康植物和特定疾病的图像,如静脉缩,干叶,Septoria棕色斑点和细菌叶病.
- 还包括了根图像,为分析提供了更广泛的范围.
结论:
- 发布的数据集是促进农业疾病图像识别的宝贵资源.
- 它促进了智能农业机器学习模型的开发和基准测试.
- 该数据集的可访问性将促进在作物健康管理领域的进一步研究和创新.
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