A two-step machine learning approach for crop disease detection: an application of GAN and UAV technology

• URL: http://arxiv.org/abs/2109.11066v1
• Date: Sun, 19 Sep 2021 03:51:20 GMT
• Title: A two-step machine learning approach for crop disease detection: an application of GAN and UAV technology
• Authors: Aaditya Prasad (1), Nikhil Mehta (1), Matthew Horak (2), Wan D. Bae (3) ((1) Tesla STEM High School, (2) Lockheed Martin Corporation, (3) Seattle University)
• Abstract summary: This paper presents a two-step machine learning approach that analyzes low-fidelity and high-fidelity images in sequence. The results show an accuracy of 96.3% for the high-fidelity system and a 75.5% confidence level for our low-fidelity system.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated plant diagnosis is a technology that promises large increases in cost-efficiency for agriculture. However, multiple problems reduce the effectiveness of drones, including the inverse relationship between resolution and speed and the lack of adequate labeled training data. This paper presents a two-step machine learning approach that analyzes low-fidelity and high-fidelity images in sequence, preserving efficiency as well as accuracy. Two data-generators are also used to minimize class imbalance in the high-fidelity dataset and to produce low-fidelity data that is representative of UAV images. The analysis of applications and methods is conducted on a database of high-fidelity apple tree images which are corrupted with class imbalance. The application begins by generating high-fidelity data using generative networks and then uses this novel data alongside the original high-fidelity data to produce low-fidelity images. A machine-learning identifier identifies plants and labels them as potentially diseased or not. A machine learning classifier is then given the potentially diseased plant images and returns actual diagnoses for these plants. The results show an accuracy of 96.3% for the high-fidelity system and a 75.5% confidence level for our low-fidelity system. Our drone technology shows promising results in accuracy when compared to labor-based methods of diagnosis.

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