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Smart Agriculture: Improving Crop Health
Subject area: Science,Engineering and Technology · Area of research: Convolutional Neural Networks
Abstract
Pest damage to plants and crops has an impact on the nation's agricultural output. In most cases, farmers or professionals watch the plants carefully for signs of illness. However, this procedure is frequently time-consuming, costly, and unreliable. Results from automatic detection employing image processing methods are quick and precise. This study uses deep convolutional networks to establish a new method for developing illness detection models that is backed by leaf image categorization. The area of precision agriculture has a possibility to grow and improve the practice of precise plant protection as well as the market for computer vision applications. A quick and simple system is made possible by the approach utilized and a wholly original manner of training.
Keywords
Plant Disease Detection, Machine Learning, Image Processing, Deep Learning, Convolutional Neural Network
How to cite this paper
@article{1704740,
author = {Prashant Chawla, Dr. K. C. Tripathi, Dr. M. L. Sharma},
title = {Smart Agriculture: Improving Crop Health},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {12},
pages = {1177-1180},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704740.pdf},
abstract = {Pest damage to plants and crops has an impact on the nation's agricultural output. In most cases, farmers or professionals watch the plants carefully for signs of illness. However, this procedure is frequently time-consuming, costly, and unreliable. Results from automatic detection employing image processing methods are quick and precise. This study uses deep convolutional networks to establish a new method for developing illness detection models that is backed by leaf image categorization. The area of precision agriculture has a possibility to grow and improve the practice of precise plant protection as well as the market for computer vision applications. A quick and simple system is made possible by the approach utilized and a wholly original manner of training.},
keywords = {Plant Disease Detection, Machine Learning, Image Processing, Deep Learning, Convolutional Neural Network},
month = {June},
}