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.