Fruit Disease Classification and Identification using Image Processing

Fruit Industry is the largest industry of India. Due to lack of maintenance, inappropriate manual inspection the fruit Disease causes huge losses in yield, quality and quantity. Manual inspection is tedious and time consuming process. An image processing approach is proposed for apple fruit disease identification and categorization using different color, texture and shape feature combination. The basic steps of the proposed approach are image segmentation, extraction of features (color, texture and shape), feature combination and finally apple disease identified and classified using multi-class support vector machine into diseased or normal class. Our proposed technique experimentally verified and validated. The accuracy of the proposed approach is achieved up to 96%.

• Publication year

  2019

• Venue

  International Conference Computing Methodologies and Communication

• Publication date

  2019-03-01

• Fields of study

  Agricultural and Food Sciences, Computer Science

• Identifiers
  DOI 10.1109/ICCMC.2019.8819789
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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