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Statistics report
Feb. 2023
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 119
Paper Published : 1427
No. of Authors : 3631
  Journal Paper


Paper Title :
A Learning Techniques of Convolutional Neural Network (CNN) for Pest Diagnosis in Grapes Crop

Author :Pushpalata Patil, Pallavi Jamsandekar

Article Citation :Pushpalata Patil ,Pallavi Jamsandekar , (2021 ) " A Learning Techniques of Convolutional Neural Network (CNN) for Pest Diagnosis in Grapes Crop " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 20-27, Volume-9,Issue-10

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

Type : Research paper

Published : Volume-9,Issue-10


DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-18206   View Here

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