Detection of Fruit Disease using Artificial N...

Detection of Fruit Disease using Artificial Neural Networks

Author Name : D. Geetha, Rajesh Kumar.P

ABSTRACT

Because there is such a high demand for agricultural products these days, effective fruit growth and better yield are vital and important. Farmers will need to manually monitor fruits from harvest through completion for this purpose. Manual monitoring, on the other hand, will not always produce excellent results, and they will always require expert counsel. As a result, it is necessary to propose an effective smart farming strategy that will aid to improve productivity and growth while requiring less human labour. We present a method for diagnosing and classifying exterior illness in fruits. The traditional approach employs thousands of words, resulting in linguistic boundaries. The system that we have devised, on the other hand, employs image processing techniques for execution because images are a simple way of expressing information. The proposed project entails OpenCV library is applied for implementation. K-means clustering method is applied for image segmentation, the images are catalogue and mapped to their respective disease categories on basis of color, texture and structure of hole on the fruit. The system uses two image databases, one for implementation of query images and the other for training of already stored disease images. Artificial Neural Network (ANN) concept is used for pattern matching and classification of diseases.

Keywords: OpenCV, K-means clustering, Artificial Neural Network.