• Akshay Thite
• Sanket Fulkar
• Hrishikesh Thite
• Rushikesh Shelar
• Anjali Almale

In previous years, several types of image analysis techniques are applied to analyse the fruit images for recognition and classification purposes .Quality needs to be defined firstly in terms of parameters or characteristics, which vary from product to product. The proposed method can process, analyse, classify and identify the fruits images, which are selected and sent in to the system based on colour, shape and size and surface features of the fruit. In this paper, we propose a web based tool that helps farmers for identifying fruit disease by uploading fruit image to the system. The system has an already trained dataset of images for the pomegranate fruit. Input image given by the user undergoes several processing steps to detect the severity of disease by comparing with the trained dataset images. First the image is resized and then its features are extracted on parameters such as color, morphology, and CCV and clustering is done by using k-means algorithm. Next, SVM is used for classification to classify the image as infected or non-infected.

Image Processing, Color, Morphology, Color Coherence Vector (CCV), Support Vector Machine (SVM) , CNN algorithm

"Smart Fruit Grading System Using Raspberry Pi", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.246-251, May-2019, Available :http://www.jetir.org/papers/JETIR1905H40.pdf

"Smart Fruit Grading System Using Raspberry Pi", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp246-251, May-2019, Available at : http://www.jetir.org/papers/JETIR1905H40.pdf