DOIONLINE 
DOIONLINE Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
 Journal Home Volume Issue |
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Issue |
Volume-9,Issue-10 ( Oct, 2021 ) | |||||||||
Paper Title |
A Learning Techniques of Convolutional Neural Network (CNN) for Pest Diagnosis in Grapes Crop | |||||||||
Author Name |
Pushpalata Patil, Pallavi Jamsandekar | |||||||||
Affilition |
Research Scholar, Bharati Vidyapeeth (Deemed to be University), PuneInstitute of Management and Rural Development Administration, Sangli, Rajwada Chowk, Sangli Incharge Director, Bharati Vidyapeeth (Deemed to be University), PuneInstitute of Management and Rural Development Administration, Sangli, Rajwada Chowk, Sangli | |||||||||
Pages |
20-27 | |||||||||
Abstract |
As India is an Agricultural based country, its economic stability depends on agriculture. Now a days due to technological advances in the digital world, production of crops has also increased, and along with this disease infection in crops has also grown. Manual detection of pests using plants parts like leaves, stem, roots is time consuming and nonavailability of timely help from experts which is time consuming and costly. Also spraying the pesticides cannot be the alltime solution as pesticides leave behind many adverse effects, such as reduction in the fertility of soil as well as health issues to the workers. To overcome these problems has led to the early detection of pests using an expert system is need of an hour. In this paper researcher has presented review of research articles based on pest detection using Image Processing, machine learning and deep learning techniques such as Convolution Neural Networks which is best suitable to extract features from the diseased leaves images and classify them. Keywords - Image Processing, Machine Learning, Deep Learning, Convolutional Neural Network, Pest Diagnosis | |||||||||
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