Advancing Cucumber Disease Detection in Agriculture through Machine Vision and Drone Technology

Autor:	Rahman, Syada Tasfia, Vasker, Nishat, Ahammed, Amir Khabbab, Hasan, Mahamudul
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	This study uses machine vision and drone technologies to propose a unique method for the diagnosis of cucumber disease in agriculture. The backbone of this research is a painstakingly curated dataset of hyperspectral photographs acquired under genuine field conditions. Unlike earlier datasets, this study included a wide variety of illness types, allowing for precise early-stage detection. The model achieves an excellent 87.5\% accuracy in distinguishing eight unique cucumber illnesses after considerable data augmentation. The incorporation of drone technology for high-resolution images improves disease evaluation. This development has enormous potential for improving crop management, lowering labor costs, and increasing agricultural productivity. This research, which automates disease detection, represents a significant step toward a more efficient and sustainable agricultural future. Comment: 10 page and 6 figure
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.12350 Zobrazit plný text záznamu View this record from Arxiv