A Two-Step Machine Learning Approach for Crop Disease Detection Using GAN and UAV Technology
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 presen...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/19/4765 |
| _version_ | 1797477197455294464 |
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| author | Aaditya Prasad Nikhil Mehta Matthew Horak Wan D. Bae |
| author_facet | Aaditya Prasad Nikhil Mehta Matthew Horak Wan D. Bae |
| author_sort | Aaditya Prasad |
| collection | DOAJ |
| description | 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 are 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 these 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. |
| first_indexed | 2024-03-09T21:14:13Z |
| format | Article |
| id | doaj.art-a9f7d2920a1544b6a2f62b31db34b189 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:14:13Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a9f7d2920a1544b6a2f62b31db34b1892023-11-23T21:38:16ZengMDPI AGRemote Sensing2072-42922022-09-011419476510.3390/rs14194765A Two-Step Machine Learning Approach for Crop Disease Detection Using GAN and UAV TechnologyAaditya Prasad0Nikhil Mehta1Matthew Horak2Wan D. Bae3Electrical Engineering, Stanford University, Stanford, CA 94305, USAComputer Science, University of Washington, Seattle, WA 99559, USALockheed Martin Space Systems, Denver, CO 80125, USAComputer Science, Seattle University, Seattle, WA 98122, USAAutomated 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 are 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 these 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.https://www.mdpi.com/2072-4292/14/19/4765automated plant disease detectionmachine learningdata augmentationunmanned aerial vehiclesgenerative adversarial networks |
| spellingShingle | Aaditya Prasad Nikhil Mehta Matthew Horak Wan D. Bae A Two-Step Machine Learning Approach for Crop Disease Detection Using GAN and UAV Technology Remote Sensing automated plant disease detection machine learning data augmentation unmanned aerial vehicles generative adversarial networks |
| title | A Two-Step Machine Learning Approach for Crop Disease Detection Using GAN and UAV Technology |
| title_full | A Two-Step Machine Learning Approach for Crop Disease Detection Using GAN and UAV Technology |
| title_fullStr | A Two-Step Machine Learning Approach for Crop Disease Detection Using GAN and UAV Technology |
| title_full_unstemmed | A Two-Step Machine Learning Approach for Crop Disease Detection Using GAN and UAV Technology |
| title_short | A Two-Step Machine Learning Approach for Crop Disease Detection Using GAN and UAV Technology |
| title_sort | two step machine learning approach for crop disease detection using gan and uav technology |
| topic | automated plant disease detection machine learning data augmentation unmanned aerial vehicles generative adversarial networks |
| url | https://www.mdpi.com/2072-4292/14/19/4765 |
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