Abstract
Dalbergia sissoo Roxb. (commonly known as Shisham in Pakistan) is a slow to medium growing tree having high economic
importance due to its fine timber quality. It is in danger in Pakistan due to various diseases like leaf blight, wilt, leaf rust, and
die-back. The traditional approach for the detection of infected leaf area requires human experts to inspect the leaves by naked
eye. This approach is time-consuming and sometimes proves to be expensive. Pathogen infection alters the physiology of trees
and results in deteriorating the wood quality which could directly affect its market value. With digital image processing
techniques, plant diseases can be detected during the early stages. Early detection of chronic Shisham diseases and appropriate
measures for treatment can help to save this plant species from extinction. This study proposes a novel technique based on
segmentation and channel detection for precise detection of infected leaf, also computing the value of a healthy and diseased
area. Early detection of Shisham chronic diseases helps to take appropriate measures to save this species, as it is already under
threat of complete evanescence. Our proposed algorithm, “Segmentation and Channel Detection (SACD)” has achieved more
than 95% accuracy in the detection of infected leaf area, which is a significant achievement for further processing
Furqan ur Rehman, Muhammad Sajjad Haider, Muhammad Ilyas, Naila Hamid, Naeem Akhter. (2020) LEAF AREA DETECTION IN DISEASED LEAVES OF DALBERGIA SISSOO(SHISHAM) USING SEGMENTATION AND CHANNEL DETECTION TECHNIQUE, Pakistan Journal of Agricultural Sciences, Volume 57, Issue 3.
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