Dataset of Citrus Canker Growth Rate through Detached Method

The hypothesis of the research was computer vision, image processing programs could be helpful in early detection and identification of citrus canker. For this purpose, initially the dataset was developed by inoculating healthy citrus leaves with disease causing organism (X. citri pv. citri) under Laboratory Controlled conditions. Briefly, six stages of citrus canker development were identified in the infected/disease images. There are total 1636 Images. These stages describe the various stages of disease development. The stages we defined in the dataset were 1) Water Soaking, 2) Yellow chlorosis/initiation (Pale Yellow/Pale Green), 3) Chlorosis, 4) Blister formation, and 5) Canker development start (50% of the inoculated area), and 6) Canker infection (100% of the inoculated area). The images were captured in Crop Diseases Research Institute (C.D.R.I.), National Agricultural Research Centre (NARC), Islamabad Pakistan regularly to measure the growth rate of citrus canker. The dataset is hosted by the Department of Computer Software Engineering, National University of Sciences and Technology-NUST Islamabad, Pakistan and acquired under the mutual cooperation of the NUST and C.D.R.I., NARC Pakistan. The dataset will be helpful for researchers for both plant pathologists and computer vision experts for classifying, detection and identification of citrus canker over specified time. The dataset was developed based on different growth stages thus it will be a novel way to monitor the disease spread. Additionally, the computer vision experts using implication of image processing, machine learning and deep learning techniques can design and build an early warning system by modeling the different disease stages and thus on site efficient robust online application can be generated which could be very useful both for farmers and agriculture department for warning and early detection system.

本研究之假设聚焦于计算机视觉领域，认为图像处理程序在柑橘溃疡病的早期检测与识别方面具有显著助益。为此，研究初期，数据集通过在实验室控制条件下，将病原体（X. citri pv. citri）接种于健康柑橘叶片而构建。概而言之，在感染/疾病图像中，确定了柑橘溃疡病发展的六个阶段。总计包含1636张图像。这些阶段详细描述了疾病发展的各个阶段。数据集中定义的阶段包括：1）水渍状，2）黄化/起始（浅黄色/浅绿色），3）黄化，4）疱斑形成，5）溃疡病发展初期（接种区域50%），以及6）溃疡病感染（接种区域100%）。图像由巴基斯坦伊斯兰堡的作物病害研究所（C.D.R.I.）、国家农业研究中心（NARC）定期采集，以测量柑橘溃疡病的生长速率。该数据集由巴基斯坦伊斯兰堡的国立科技大学（NUST）计算机软件工程系托管，并由NUST与C.D.R.I.、NARC巴基斯坦的共同努力下获得。该数据集对于植物病理学家和计算机视觉专家在指定时间内对柑橘溃疡病的分类、检测和识别工作具有重要作用。数据集基于不同的生长阶段开发，从而为监测疾病扩散提供了一种新颖的方法。此外，运用图像处理、机器学习和深度学习技术，计算机视觉专家可以依据不同的疾病阶段构建早期预警系统，从而生成现场高效、鲁棒的在线应用程序，这对于农民和农业部门在预警和早期检测系统中将极为有益。