Advancing Cucumber Disease Detection in Agriculture through Machine Vision and Drone Technology
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...
Gespeichert in:
Hauptverfasser: | , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | 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. |
---|---|
DOI: | 10.48550/arxiv.2409.12350 |