Dataset of Citrus Canker Growth Rate
Description
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 natural field conditions. Inoculation was done on susceptible citrus cultivars, C. paradisi (Grapefruit). The leaves were inoculated by injecting the bacterial suspension into fully expanded, immature leaves with needleless syringes. 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, Military College of Signals-NUST Islamabad, Pakistan and acquired under the mutual cooperation of the NUST and C.D.R.I., NARC Pakistan. The inoculated leaves images were further categorized into different stages. It defines the temporal change in citrus canker growth rate. Canker symptoms developed were noted daily (from day 1 to day 21). From this dataset we can estimate and predict the disease prevalence and spread in citrus orchard over a specified time and can develop an alarming system for monitoring and control of the disease before it reaches its maximum loss. 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 and identify the threshold level of the disease and take precautionary measures before it completely damages the whole fruit plant. 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. The dataset has immense potential to be use in various field of application. Plant pathologist can use this data for measuring and identification of citrus canker and further explore the effect of other environmental variable on disease development.
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Steps to reproduce
The dataset was developed by inoculating healthy citrus leaves with disease causing organism (X. citri pv. citri) under natural field conditions. Inoculation was done on susceptible citrus cultivars, C. paradisi (Grapefruit). The leaves were inoculated by injecting the bacterial suspension into fully expanded, immature leaves with needleless syringes. 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, Military College of Signals-NUST Islamabad, Pakistan and acquired under the mutual cooperation of the NUST and C.D.R.I., NARC Pakistan. The inoculated leaves images were further categorized into different stages. It defines the temporal change in citrus canker growth rate. Canker symptoms developed were noted daily (from day 1 to day 21). From this dataset we can estimate and predict the disease prevalence and spread in citrus orchard over a specified time and can develop an alarming system for monitoring and control of the disease before it reaches its maximum loss. The dataset was gathered using an advanced DSLR (NIKON D3500) with CMOS sensor. The sensor size for NIKON D3500 was 23.5X15.6 (mm). The ideal micro lens for the DSLR Nikon D3500 Nikon 40MM was F2.8AF-DSX macro lens. All images were resized to 6000 x 4000 with 300 dpi resolution.