Computer vision for plant pathology: A review with examples from cocoa agriculture
Abstract Plant pathogens can decimate crops and render the local cultivation of a species unprofitable. In extreme cases this has caused famine and economic collapse. Timing is vital in treating crop diseases, and the use of computer vision for precise disease detection and timing of pesticide appli...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-03-01
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| Series: | Applications in Plant Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aps3.11559 |
| _version_ | 1797202602711056384 |
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| author | Jamie R. Sykes Katherine J. Denby Daniel W. Franks |
| author_facet | Jamie R. Sykes Katherine J. Denby Daniel W. Franks |
| author_sort | Jamie R. Sykes |
| collection | DOAJ |
| description | Abstract Plant pathogens can decimate crops and render the local cultivation of a species unprofitable. In extreme cases this has caused famine and economic collapse. Timing is vital in treating crop diseases, and the use of computer vision for precise disease detection and timing of pesticide application is gaining popularity. Computer vision can reduce labour costs, prevent misdiagnosis of disease, and prevent misapplication of pesticides. Pesticide misapplication is both financially costly and can exacerbate pesticide resistance and pollution. Here, we review the application and development of computer vision and machine learning methods for the detection of plant disease. This review goes beyond the scope of previous works to discuss important technical concepts and considerations when applying computer vision to plant pathology. We present new case studies on adapting standard computer vision methods and review techniques for acquiring training data, the use of diagnostic tools from biology, and the inspection of informative features. In addition to an in‐depth discussion of convolutional neural networks (CNNs) and transformers, we also highlight the strengths of methods such as support vector machines and evolved neural networks. We discuss the benefits of carefully curating training data and consider situations where less computationally expensive techniques are advantageous. This includes a comparison of popular model architectures and a guide to their implementation. |
| first_indexed | 2024-04-24T08:06:03Z |
| format | Article |
| id | doaj.art-5d0bc986d91b423ea1cbc066d3f8984f |
| institution | Directory Open Access Journal |
| issn | 2168-0450 |
| language | English |
| last_indexed | 2024-04-24T08:06:03Z |
| publishDate | 2024-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applications in Plant Sciences |
| spelling | doaj.art-5d0bc986d91b423ea1cbc066d3f8984f2024-04-17T09:56:36ZengWileyApplications in Plant Sciences2168-04502024-03-01122n/an/a10.1002/aps3.11559Computer vision for plant pathology: A review with examples from cocoa agricultureJamie R. Sykes0Katherine J. Denby1Daniel W. Franks2Department of Computer Science University of York Deramore Lane, York YO10 5GH Yorkshire United KingdomCentre for Novel Agricultural Products, Department of Biology University of York Wentworth Way, York YO10 5DD Yorkshire United KingdomDepartment of Computer Science University of York Deramore Lane, York YO10 5GH Yorkshire United KingdomAbstract Plant pathogens can decimate crops and render the local cultivation of a species unprofitable. In extreme cases this has caused famine and economic collapse. Timing is vital in treating crop diseases, and the use of computer vision for precise disease detection and timing of pesticide application is gaining popularity. Computer vision can reduce labour costs, prevent misdiagnosis of disease, and prevent misapplication of pesticides. Pesticide misapplication is both financially costly and can exacerbate pesticide resistance and pollution. Here, we review the application and development of computer vision and machine learning methods for the detection of plant disease. This review goes beyond the scope of previous works to discuss important technical concepts and considerations when applying computer vision to plant pathology. We present new case studies on adapting standard computer vision methods and review techniques for acquiring training data, the use of diagnostic tools from biology, and the inspection of informative features. In addition to an in‐depth discussion of convolutional neural networks (CNNs) and transformers, we also highlight the strengths of methods such as support vector machines and evolved neural networks. We discuss the benefits of carefully curating training data and consider situations where less computationally expensive techniques are advantageous. This includes a comparison of popular model architectures and a guide to their implementation.https://doi.org/10.1002/aps3.11559agronomydisease detectionmachine learningplant pathology |
| spellingShingle | Jamie R. Sykes Katherine J. Denby Daniel W. Franks Computer vision for plant pathology: A review with examples from cocoa agriculture Applications in Plant Sciences agronomy disease detection machine learning plant pathology |
| title | Computer vision for plant pathology: A review with examples from cocoa agriculture |
| title_full | Computer vision for plant pathology: A review with examples from cocoa agriculture |
| title_fullStr | Computer vision for plant pathology: A review with examples from cocoa agriculture |
| title_full_unstemmed | Computer vision for plant pathology: A review with examples from cocoa agriculture |
| title_short | Computer vision for plant pathology: A review with examples from cocoa agriculture |
| title_sort | computer vision for plant pathology a review with examples from cocoa agriculture |
| topic | agronomy disease detection machine learning plant pathology |
| url | https://doi.org/10.1002/aps3.11559 |
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