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arXiv · 2409.12350

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

Syada Tasfia Rahman·Nishat Vasker·Amir Khabbab Ahammed·Mahamudul Hasan
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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.

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Syada Tasfia Rahman, Nishat Vasker, Amir Khabbab Ahammed, Mahamudul Hasan. 2024-09-18. Advancing Cucumber Disease Detection in Agriculture through Machine Vision and Drone Technology. https://arxiv.org/abs/2409.12350

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