Publication Type:

Conference Paper

Source:

2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), p.1040-1043 (2019)

URL:

https://ieeexplore.ieee.org/abstract/document/8822095

Keywords:

agricultural safety, agriculture, apple fruits, banana fruits, cherry fruits, Conferences, convolutional neural nets, Convolutional neural network, Convolutional neural networks, Crop yield, Crops, disease detection, disease detection., Diseases, economic loss, Food products, Fruit disease, fruit recognition, image classification, Image color analysis, Image processing, Inception V3 model, India, learning (artificial intelligence), mathematical model, Plant Diseases, Tensor flow platform, TensorFlow, Training, transfer learning technique, user-friendly tool

Abstract:

In India, crop yield is declined due to the post-recognition of diseases in fruits/vegetables by the farmers. Farmers face great economic loss worldwide. Diseases in fruits and plants are the main reasons for the agricultural loss. Knowing the health status of fruits/vegetables helps farmers to improve their productivity. This motivates us to design and develop a tool to help farmers detect the diseases in the early stage itself. This work focuses on developing a user-friendly tool which recognizes the level of the disease and grades them accordingly. Inception model uses convolutional neural networks for the classification, which is again retrained using transfer learning technique. The proposed system also grades the fruit based on the percentage of infection. The system is developed in Tensor flow platform. For the proposed work banana, apple and cherry fruits have been considered.

Cite this Research Publication

M. Nikhitha, S. Sri, R., and B. Uma Maheswari, “Fruit Recognition and Grade of Disease Detection using Inception V3 Model”, in 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), 2019, pp. 1040-1043.