Earticle

초록

영어

Maize is known as one of the healthiest diets in the world, but its productivity is critically harmed by various diseases, with blight, common rust, and gray leaf spot being the most common. Early and accurate detection of these diseases is challenging. We have developed a CNN-based Sequential Model for disease classification, which aids farmers in applying appropriate treatments. Although maize is a vital global staple, its productivity is often threatened by viral leaf diseases, leading to substantial yield losses. Timely and accurate detection of these diseases is essential for effective crop management. This study introduces a deep neural network (DNN) designed to identify maize leaf diseases—specifically Blight, Gray Leaf Spot, and Common Rust—by extracting complex image features. An attention mechanism helps the model focus on critical image areas, enhancing interpretability and robustness. Validation experiments demonstrate the model's efficiency, confirming its potential as a reliable tool for precision agriculture.

목차

Abstract
I. INTRODUCTION
II. PROBLEM STATEMENT
III. OBJECTIVES
IV. METHODOLOGY
V. RESULTS AND DISCUSSION
CONCLUSION
REFERENCES

키워드

저자

• Arif Wicaksono Septyanto [ Information Systems Institut Teknologi Kalimantan Balikpapan, Indonesia ]
• Muhammad Umair [ Green International Univeristy Lahore, Lahore, Pakistan ]
• Bilal Shoaib Khan [ Green International University Lahore, Pakistan. ]
• Abdul Hannan Khan [ Green International Univeristy Lahore, Lahore, Pakistan ]
• Muhammad Usman Abbas [ Muhammad Usman Abbas Green International University Lahore Lahore, 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