Strawberry plant wetness detection using computer vision and deep learning
Botrytis fruit rot and anthracnose are fungal diseases of strawberry. These diseases are a significant contributor to yield losses, requiring farmers to use fungicides frequently to prevent them. The proliferation of botrytis and anthracnose is directly linked to the duration of the presence of free...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375521000137 |
| _version_ | 1818345028201742336 |
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| author | Arth Patel Won Suk Lee Natalia A. Peres Clyde W. Fraisse |
| author_facet | Arth Patel Won Suk Lee Natalia A. Peres Clyde W. Fraisse |
| author_sort | Arth Patel |
| collection | DOAJ |
| description | Botrytis fruit rot and anthracnose are fungal diseases of strawberry. These diseases are a significant contributor to yield losses, requiring farmers to use fungicides frequently to prevent them. The proliferation of botrytis and anthracnose is directly linked to the duration of the presence of free water on the plant canopy, which is generally defined as leaf wetness duration (LWD). LWD is an important measure in determining the risk for these diseases to develop in the strawberry crop. By accurately measuring LWD, the risk of disease can be calculated more accurately, and specific fungicide application recommendations can be given to the farmers. This reduces the frequency with which fungicide is applied and ultimately reduces costs for farmers. There is no standard method to detect leaf wetness, but leaf wetness sensors are widely used for that purpose. These wetness sensors are difficult to calibrate and not very accurate, which reduces their reliability. The objective of this study was to find a better alternative to the commonly used leaf wetness sensors. This study implemented color and thermal imaging-based approaches as a solution to the problem of leaf wetness detection in strawberry plants. The proposed method used deep learning and computer vision techniques to detect leaf wetness from color and thermal images. The deep learning model was highly accurate in detecting wetness when compared with the visual observation of the images. It was also found that leaf wetness could be detected with a high degree of accuracy using deep learning with color images. In the future, using the findings of this study, a portable device can be developed to replace the commonly used wetness sensor with a more reliable imaging-based device. |
| first_indexed | 2024-12-13T16:55:51Z |
| format | Article |
| id | doaj.art-019d5428b815445d947744101b8e2a3f |
| institution | Directory Open Access Journal |
| issn | 2772-3755 |
| language | English |
| last_indexed | 2024-12-13T16:55:51Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj.art-019d5428b815445d947744101b8e2a3f2022-12-21T23:37:54ZengElsevierSmart Agricultural Technology2772-37552021-12-011100013Strawberry plant wetness detection using computer vision and deep learningArth Patel0Won Suk Lee1Natalia A. Peres2Clyde W. Fraisse3Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, United StatesDepartment of Agricultural and Biological Engineering, Rogers Hall, Museum Road, University of Florida, Gainesville, FL 32611, United States; Corresponding author at: Department of Agriculture and Biological Engineering, University of Florida, Gainesville, FL 32611, United StatesGulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, United StatesDepartment of Agricultural and Biological Engineering, Rogers Hall, Museum Road, University of Florida, Gainesville, FL 32611, United StatesBotrytis fruit rot and anthracnose are fungal diseases of strawberry. These diseases are a significant contributor to yield losses, requiring farmers to use fungicides frequently to prevent them. The proliferation of botrytis and anthracnose is directly linked to the duration of the presence of free water on the plant canopy, which is generally defined as leaf wetness duration (LWD). LWD is an important measure in determining the risk for these diseases to develop in the strawberry crop. By accurately measuring LWD, the risk of disease can be calculated more accurately, and specific fungicide application recommendations can be given to the farmers. This reduces the frequency with which fungicide is applied and ultimately reduces costs for farmers. There is no standard method to detect leaf wetness, but leaf wetness sensors are widely used for that purpose. These wetness sensors are difficult to calibrate and not very accurate, which reduces their reliability. The objective of this study was to find a better alternative to the commonly used leaf wetness sensors. This study implemented color and thermal imaging-based approaches as a solution to the problem of leaf wetness detection in strawberry plants. The proposed method used deep learning and computer vision techniques to detect leaf wetness from color and thermal images. The deep learning model was highly accurate in detecting wetness when compared with the visual observation of the images. It was also found that leaf wetness could be detected with a high degree of accuracy using deep learning with color images. In the future, using the findings of this study, a portable device can be developed to replace the commonly used wetness sensor with a more reliable imaging-based device.http://www.sciencedirect.com/science/article/pii/S2772375521000137Color imagingStrawberry Advisory SystemStrawberry diseaseThermal imaging |
| spellingShingle | Arth Patel Won Suk Lee Natalia A. Peres Clyde W. Fraisse Strawberry plant wetness detection using computer vision and deep learning Smart Agricultural Technology Color imaging Strawberry Advisory System Strawberry disease Thermal imaging |
| title | Strawberry plant wetness detection using computer vision and deep learning |
| title_full | Strawberry plant wetness detection using computer vision and deep learning |
| title_fullStr | Strawberry plant wetness detection using computer vision and deep learning |
| title_full_unstemmed | Strawberry plant wetness detection using computer vision and deep learning |
| title_short | Strawberry plant wetness detection using computer vision and deep learning |
| title_sort | strawberry plant wetness detection using computer vision and deep learning |
| topic | Color imaging Strawberry Advisory System Strawberry disease Thermal imaging |
| url | http://www.sciencedirect.com/science/article/pii/S2772375521000137 |
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