A survey on detection and classification of cotton leaf diseases

• Published in: 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) pp. 2499 - 2506
• Main Authors: Prajapati, Bhumika S., Dabhi, Vipul K., Prajapati, Harshadkumar B.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-03-2016
• Subjects: Cotton; Diseases; Feature extraction; Image pre-processing; Image segmentation; Leaf diseases; Microorganisms; Otsu's thresholding; Support Vector Machine (SVM); Support vector machines
• DOI: 10.1109/ICEEOT.2016.7755143

Cotton is the most important cash crop in India. It is also known as "White Gold" or "The King of fibers" among all cash crops in the country. About 80-90% of the diseases which occur on the leaves of cotton are Alternaria leaf spot, Cercospora leaf spot, Bacterial blight and Red spot. This paper presents a survey on detection and classification of cotton leaf diseases. It is difficult for human eyes to identify the exact type of leaf disease which occurs on the leaf of plant. Thus, in order to identify the cotton leaf diseases accurately, the use of image processing and machine learning techniques can be helpful. The images used for this work were acquired from the cotton field using digital camera. In pre-processing step, background removal technique is applied on the image in order to remove background from the image. Then, the background removed images are further processed for image segmentation using otsu thresholding technique. Different segmented images will be used for extracting the features such as color, shape and texture from the images. At last, these extracted features will be used as inputs of classifier.