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

TL;DR

The model achieves an excellent 87.5\% accuracy in distinguishing eight unique cucumber illnesses after considerable data augmentation, and the incorporation of drone technology for high-resolution images improves disease evaluation.

Abstract

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.

Figures (6)

• Figure 1: Distribution of eight cucumber diseases.
• Figure 2: Augmented image of different disease class
• Figure 3: Drone setup
• Figure 4: Working flowchart
• Figure 5: Training and validation accuracy and loss