Detection and Classification of Ring, Rust and Yellow Sugarcane Leaf Diseases
Anoop G L1, C. Nandini2

1Anoop G L, Computer Science and Engineering, Dayananda Sagar Academy of Technology & Management, Bangalore, India.
2C. Nandini, Computer Science & Engineering, Dayananda Sagar Academy of Technology & Management, Bangalore, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 3310-3315 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9314088619/2019©BEIESP | DOI: 10.35940/ijeat.F9314.088619
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Abstract: Agriculture is an important sector in Economic and Social life. Crop disease detection is an emerging field in India. We can minimize the diseases infection on sugarcane leaf by detecting and grading the leaf disease in early stages. In this paper, we are detecting and recognize Sugar cane leaf diseases by using grey scale and color image processing and analyze the efficacy by comparing both. In grey scale processing, we presented Gradient Magnitude, Otsu method, Morphological Operations and Normalization to extract the Region of interest (ROI) i.e., disease part. In color processing initially converted RGB to L*a*b format, later K-means clustering and edge detection operations are applied on L*a*b image format. The features of Grey scale & color processed image are extracted and feed to Support Vector Machine (SVM) classifier which classifies ring, rust & yellow spot sugarcane leaf diseases. The Sugarcane leaf diseases are classified successfully with an average accuracy of 84% & 92% for grey scale & color features respectively.
Keywords: Color Processing, Gradient Magnitude, Grey Scale Processing, K-means clustering, Normalization.