Advancing Cucumber Disease Detection in Agriculture through Machine Vision and Drone Technology
This work addresses crop management and labor cost reduction in agriculture, but it is incremental as it builds on existing technologies for a specific domain.
The study tackled cucumber disease detection by using machine vision and drone technology on a curated hyperspectral dataset, achieving 87.5% accuracy in distinguishing eight diseases.
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.