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

• URL: http://arxiv.org/abs/2409.12350v1
• Date: Wed, 18 Sep 2024 22:54:23 GMT
• Title: Advancing Cucumber Disease Detection in Agriculture through Machine Vision and Drone Technology
• Authors: Syada Tasfia Rahman, Nishat Vasker, Amir Khabbab Ahammed, Mahamudul Hasan,
• Abstract summary: 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. The model achieves an excellent 87.5% accuracy in distinguishing eight unique cucumber illnesses.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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