FRUIT DISEASE DETECTION USING COLOR AND TEXTURE ANALYSIS BY CNN

FRUIT DISEASE DETECTION USING COLOR AND TEXTURE ANALYSIS BY CNN

1 P Uma Devi 2  P. Pavithra

1MCA Student, Department of Master of Computer Applications,

Vignan’s Institute of Information Technology(A), Beside VSEZ, Duvvada, Vadlapudi Post,

Gajuwaka, Visakhapatnam-530049.

2Assistant Professor, Department of Information Technology,

Vignan’s Institute of Information Technology(A), Beside VSEZ, Duvvada, Vadlapudi Post, Gajuwaka, Visakhapatnam-530049.

ABSTRACT:

Now-a-days as there is prohibitive demand for agricultural industry, effective growth and improved yield of fruit is necessary and important. For this purpose farmers need manual monitoring of fruits from harvest till its progress period. But manual monitoring will not give satisfactory result all the times and they always need satisfactory advice from expert. So it requires proposing an efficient smart farming technique which will help for better yield and growth with less human efforts. We introduce a technique which will diagnose and classify external disease within fruits. Whereas system that we have come up with, uses image processing techniques for implementation as image is easy way for conveying. In the proposed work, OpenCV library is applied for implementation, the images are catalogue and mapped to their respective disease categories on basis of four feature vectors colour, morphology, texture and structure of hole on the fruit. Convolutional Neural Network (CNN) concept is used for pattern matching and classification of diseases..

Keywords: CNN Algorithm ,OpenCV ,Image processing techniques