Earticle

초록

영어

Sugarcane is an essential crop in the global agriculture industry. There are lot of diseases in plants of growing sugarcane typically involve in five classes. These diseases consist of Mosaic, Red rot, Yellow, Rust and Healthy. Therefore, this study used to train and testi a deep learning model comprising of 2521 Sugar cane image dataset of disease-infected leaves. This research provides a sequential model for the classification of sugar cane using convolutional neural network. This study used sequential network in which ten layers are adjusted for the classification of these Mosaic, Red rot, yellow, Rust and healthy diseases. The accuracy of the proposed method works better in comparison with the previously used techniques.

목차

Abstract
I. INTRODUCTION
II. PROPOSED METHODOLOGY
III. RESULTS AND DISCUSSION
IV. CONCLUSION AND FUTURE WORKS
REFERENCES

키워드

저자

• Muhammad Usman Abbas [ Green International University Lahore Lahore, Pakistan ]
• Bilal Shoaib Khan [ Green International University Lahore Lahore, Pakistan ]
• Abdul Hanan Khan [ Green International University Lahore, Pakistan. ]
• Muhammad Umair [ Green International Univeristy Lahore, Lahore, Pakistan ]
• Muhammad Asamullah Ulfat [ Akhuwat College University, Kasur, Pakistan ]
• Muhammad Adnan Khan [ School of Computing, Skyline University College, Sharjah, UAE. RSCI, Riphah International University, Lahore Campus, Lahore, Pakistan. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004