Automated Health Assessment of Betel (piper betle l.) Leaves using Correlation-based Image Analysis and Disease Severity Estimation

SANDIP BORDOLOI

Abstract


Early detection of foliar diseases is essential for maintaining the quality and commercial value of betel (Piper betle L.) leaves. Conventional visual inspection is subjective, time-consuming, and dependent on expert knowledge. This study presents an automated image-based approach for the classification of betel leaf health and estimation of disease severity using digital image processing techniques implemented in the R programming environment. The proposed method employs the Pliman package for image preprocessing, resizing, blue-channel index extraction, and disease quantification. A healthy reference leaf image was used to compute the Pearson correlation coefficient between the blue-channel intensity distributions of test and reference images. Based on predefined similarity thresholds, the leaves were categorized as Healthy, Diseased, Torn/Other, or Unclassified. Disease severity was further estimated using healthy and symptomatic reference images. Experimental evaluation was carried out on eleven betel leaf images comprising healthy and diseased samples. The healthy reference image exhibited a similarity score of 1.0, whereas diseased leaves showed similarity values between approximately 0.70 and 0.82. The estimated symptomatic disease severity ranged from approximately 66% to 97%, demonstrating the capability of the proposed approach to distinguish healthy and infected leaves effectively. The developed method provides a rapid, non-destructive, and computationally efficient tool for preliminary disease assessment and has potential applications in precision agriculture, smart farming, and automated crop monitoring system.


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References


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