Deep learning‐ and image processing‐based methods for automatic estimation of leaf herbivore damage
Abstract Quantifying the intensity of leaf herbivory pressure is crucial for understanding the interaction between plants and herbivores in both applied and basic science. Visual estimates and digital analysis have been commonly used to estimate leaf herbivore damage but are time‐consuming which lim...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-04-01
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| Series: | Methods in Ecology and Evolution |
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| Online Access: | https://doi.org/10.1111/2041-210X.14293 |
| _version_ | 1827295797009448960 |
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| author | Zihui Wang Yuan Jiang Abdoulaye Baniré Diallo Steven W. Kembel |
| author_facet | Zihui Wang Yuan Jiang Abdoulaye Baniré Diallo Steven W. Kembel |
| author_sort | Zihui Wang |
| collection | DOAJ |
| description | Abstract Quantifying the intensity of leaf herbivory pressure is crucial for understanding the interaction between plants and herbivores in both applied and basic science. Visual estimates and digital analysis have been commonly used to estimate leaf herbivore damage but are time‐consuming which limits the amount of data that can be collected and prevent answering big picture questions that require large‐scale sampling of herbivory pressure. Recent developments in deep learning have provided a potential tool for automatic collection of ecological data from various sources. However, most applications have focused on identification and counting, and there is a lack of deep learning tools for quantitative estimation of leaf herbivore damage. Here, we trained generative adversarial networks (GANs) to predict the intact status of damaged leaves and applied image processing technique to estimate the area and percentage of leaf damage. We first described procedures for collecting leaf images, training GAN models, predicting intact leaves and calculating leaf area, with a Python package provided to enable hands‐on application of these procedures. Then, we collected a large leaf data set to train a universal deep learning model and developed an online app HerbiEstim to allow direct use of pretrained models to estimate herbivory damage of leaves. We tested these methods using both simulated and real leaf damage data. The procedures provided in our study greatly improved the efficiency of leaf herbivore damage estimation. Our test demonstrated that the reconstruction of damaged leaf image resembled the ground‐truth image with a similarity of 98.8%. The estimation of leaf herbivore damage exhibited a high accuracy with an averaged root mean square error of 1.6% and had a general applicability to different plant taxa and leaf shapes. Overall, our work demonstrated the feasibility of applying deep learning techniques to quantify leaf herbivory intensity. The use of GANs allows automatic estimation of leaf damage, representing a major advantage of the method. The Python package and the online app with pre‐trained models will facilitate the use of our method for the analysis of large data sets of plant–herbivore interactions. |
| first_indexed | 2024-04-24T14:27:29Z |
| format | Article |
| id | doaj.art-ff13a50b55024d1a97f19640e6af28e1 |
| institution | Directory Open Access Journal |
| issn | 2041-210X |
| language | English |
| last_indexed | 2024-04-24T14:27:29Z |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj.art-ff13a50b55024d1a97f19640e6af28e12024-04-03T04:38:58ZengWileyMethods in Ecology and Evolution2041-210X2024-04-0115473274310.1111/2041-210X.14293Deep learning‐ and image processing‐based methods for automatic estimation of leaf herbivore damageZihui Wang0Yuan Jiang1Abdoulaye Baniré Diallo2Steven W. Kembel3Département des sciences biologiques Université du Québec à Montréal Montréal Québec CanadaSchool of Ecology Sun Yat‐sen University Guangzhou ChinaDépartement d'informatique Université du Québec à Montréal Montréal Québec CanadaDépartement des sciences biologiques Université du Québec à Montréal Montréal Québec CanadaAbstract Quantifying the intensity of leaf herbivory pressure is crucial for understanding the interaction between plants and herbivores in both applied and basic science. Visual estimates and digital analysis have been commonly used to estimate leaf herbivore damage but are time‐consuming which limits the amount of data that can be collected and prevent answering big picture questions that require large‐scale sampling of herbivory pressure. Recent developments in deep learning have provided a potential tool for automatic collection of ecological data from various sources. However, most applications have focused on identification and counting, and there is a lack of deep learning tools for quantitative estimation of leaf herbivore damage. Here, we trained generative adversarial networks (GANs) to predict the intact status of damaged leaves and applied image processing technique to estimate the area and percentage of leaf damage. We first described procedures for collecting leaf images, training GAN models, predicting intact leaves and calculating leaf area, with a Python package provided to enable hands‐on application of these procedures. Then, we collected a large leaf data set to train a universal deep learning model and developed an online app HerbiEstim to allow direct use of pretrained models to estimate herbivory damage of leaves. We tested these methods using both simulated and real leaf damage data. The procedures provided in our study greatly improved the efficiency of leaf herbivore damage estimation. Our test demonstrated that the reconstruction of damaged leaf image resembled the ground‐truth image with a similarity of 98.8%. The estimation of leaf herbivore damage exhibited a high accuracy with an averaged root mean square error of 1.6% and had a general applicability to different plant taxa and leaf shapes. Overall, our work demonstrated the feasibility of applying deep learning techniques to quantify leaf herbivory intensity. The use of GANs allows automatic estimation of leaf damage, representing a major advantage of the method. The Python package and the online app with pre‐trained models will facilitate the use of our method for the analysis of large data sets of plant–herbivore interactions.https://doi.org/10.1111/2041-210X.14293data collectiondeep learninggenerative adversarial networkimage processingleaf herbivore damage |
| spellingShingle | Zihui Wang Yuan Jiang Abdoulaye Baniré Diallo Steven W. Kembel Deep learning‐ and image processing‐based methods for automatic estimation of leaf herbivore damage Methods in Ecology and Evolution data collection deep learning generative adversarial network image processing leaf herbivore damage |
| title | Deep learning‐ and image processing‐based methods for automatic estimation of leaf herbivore damage |
| title_full | Deep learning‐ and image processing‐based methods for automatic estimation of leaf herbivore damage |
| title_fullStr | Deep learning‐ and image processing‐based methods for automatic estimation of leaf herbivore damage |
| title_full_unstemmed | Deep learning‐ and image processing‐based methods for automatic estimation of leaf herbivore damage |
| title_short | Deep learning‐ and image processing‐based methods for automatic estimation of leaf herbivore damage |
| title_sort | deep learning and image processing based methods for automatic estimation of leaf herbivore damage |
| topic | data collection deep learning generative adversarial network image processing leaf herbivore damage |
| url | https://doi.org/10.1111/2041-210X.14293 |
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