Deep learning‐ and image processing‐based methods for automatic estimation of leaf herbivore damage Article Swipe
Zihui Wang
,
Yuan Jiang
,
Abdoulaye Baniré Diallo
,
Steven W. Kembel
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1111/2041-210x.14293
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1111/2041-210x.14293
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/2041-210x.14293
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/2041-210X.14293
- OA Status
- gold
- Cited By
- 6
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392237125
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392237125Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/2041-210x.14293Digital Object Identifier
- Title
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Deep learning‐ and image processing‐based methods for automatic estimation of leaf herbivore damageWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-02-27Full publication date if available
- Authors
-
Zihui Wang, Yuan Jiang, Abdoulaye Baniré Diallo, Steven W. KembelList of authors in order
- Landing page
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https://doi.org/10.1111/2041-210x.14293Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/2041-210X.14293Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/2041-210X.14293Direct OA link when available
- Concepts
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Herbivore, Ground truth, Python (programming language), Artificial intelligence, Computer science, Deep learning, Machine learning, Identification (biology), Pattern recognition (psychology), Ecology, Biology, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2025: 6Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2286959103, https://openalex.org/W2076306922, https://openalex.org/W2765811365, https://openalex.org/W2902012839, https://openalex.org/W2004827430, https://openalex.org/W3127708089, https://openalex.org/W2963073614, https://openalex.org/W2029796932, https://openalex.org/W2175804398, https://openalex.org/W2045425845, https://openalex.org/W2521164240, https://openalex.org/W2034893675, https://openalex.org/W2769210209, https://openalex.org/W3000202082, https://openalex.org/W6911157701, https://openalex.org/W2133665775, https://openalex.org/W1968937938, https://openalex.org/W2783106047, https://openalex.org/W2962793481, https://openalex.org/W3038699438, https://openalex.org/W4200268964, https://openalex.org/W2992168095, https://openalex.org/W2952113774, https://openalex.org/W2173854188, https://openalex.org/W2195515819, https://openalex.org/W2172289437, https://openalex.org/W2213241010, https://openalex.org/W2954932437, https://openalex.org/W3114427951, https://openalex.org/W3005940314, https://openalex.org/W2102151056, https://openalex.org/W4220945537 |
| referenced_works_count | 32 |
| abstract_inverted_index.a | 71, 95, 156, 170, 177, 243, 254, 268, 305 |
| abstract_inverted_index.We | 137, 201 |
| abstract_inverted_index.an | 184, 258 |
| abstract_inverted_index.be | 49 |
| abstract_inverted_index.in | 18, 66, 216 |
| abstract_inverted_index.is | 8, 94 |
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| abstract_inverted_index.we | 109, 168 |
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| abstract_inverted_index.Our | 228 |
| abstract_inverted_index.The | 213, 247, 294, 311 |
| abstract_inverted_index.and | 16, 21, 26, 51, 90, 92, 123, 132, 151, 182, 208, 266, 275, 314 |
| abstract_inverted_index.app | 186, 317 |
| abstract_inverted_index.are | 39 |
| abstract_inverted_index.big | 54 |
| abstract_inverted_index.but | 38 |
| abstract_inverted_index.can | 48 |
| abstract_inverted_index.for | 10, 74, 101, 141, 328 |
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| abstract_inverted_index.1.6% | 265 |
| abstract_inverted_index.GANs | 297 |
| abstract_inverted_index.area | 131 |
| abstract_inverted_index.been | 30 |
| abstract_inverted_index.both | 19, 206 |
| abstract_inverted_index.data | 46, 79, 173, 333 |
| abstract_inverted_index.deep | 67, 98, 179, 286 |
| abstract_inverted_index.from | 80 |
| abstract_inverted_index.have | 29, 69, 86 |
| abstract_inverted_index.high | 255 |
| abstract_inverted_index.lack | 96 |
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| abstract_inverted_index.sets | 334 |
| abstract_inverted_index.taxa | 274 |
| abstract_inverted_index.test | 229 |
| abstract_inverted_index.that | 47, 57, 231 |
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| abstract_inverted_index.work | 280 |
| abstract_inverted_index.Here, | 108 |
| abstract_inverted_index.Then, | 167 |
| abstract_inverted_index.allow | 189 |
| abstract_inverted_index.area, | 154 |
| abstract_inverted_index.basic | 22 |
| abstract_inverted_index.data. | 212 |
| abstract_inverted_index.error | 263 |
| abstract_inverted_index.first | 138 |
| abstract_inverted_index.image | 125, 237, 241 |
| abstract_inverted_index.large | 171, 332 |
| abstract_inverted_index.major | 306 |
| abstract_inverted_index.model | 181 |
| abstract_inverted_index.plant | 273 |
| abstract_inverted_index.study | 218 |
| abstract_inverted_index.there | 93 |
| abstract_inverted_index.these | 165, 203 |
| abstract_inverted_index.tools | 100 |
| abstract_inverted_index.train | 176 |
| abstract_inverted_index.using | 205 |
| abstract_inverted_index.which | 41 |
| abstract_inverted_index.(GANs) | 114 |
| abstract_inverted_index.98.8%. | 246 |
| abstract_inverted_index.Python | 157, 312 |
| abstract_inverted_index.Recent | 64 |
| abstract_inverted_index.Visual | 24 |
| abstract_inverted_index.allows | 298 |
| abstract_inverted_index.amount | 44 |
| abstract_inverted_index.damage | 37, 198, 211, 226, 252 |
| abstract_inverted_index.direct | 190 |
| abstract_inverted_index.enable | 161 |
| abstract_inverted_index.intact | 118, 149 |
| abstract_inverted_index.leaves | 122, 150 |
| abstract_inverted_index.limits | 42 |
| abstract_inverted_index.method | 327 |
| abstract_inverted_index.models | 194, 320 |
| abstract_inverted_index.online | 185, 316 |
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| abstract_inverted_index.square | 262 |
| abstract_inverted_index.status | 119 |
| abstract_inverted_index.tested | 202 |
| abstract_inverted_index.applied | 20, 124 |
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| abstract_inverted_index.crucial | 9 |
| abstract_inverted_index.damage, | 303 |
| abstract_inverted_index.damage. | 107, 136 |
| abstract_inverted_index.damaged | 121, 235 |
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| abstract_inverted_index.focused | 87 |
| abstract_inverted_index.general | 269 |
| abstract_inverted_index.greatly | 219 |
| abstract_inverted_index.images, | 144 |
| abstract_inverted_index.leaves. | 200 |
| abstract_inverted_index.method. | 310 |
| abstract_inverted_index.methods | 204 |
| abstract_inverted_index.models, | 147 |
| abstract_inverted_index.package | 158, 313 |
| abstract_inverted_index.picture | 55 |
| abstract_inverted_index.predict | 116 |
| abstract_inverted_index.prevent | 52 |
| abstract_inverted_index.require | 58 |
| abstract_inverted_index.shapes. | 277 |
| abstract_inverted_index.trained | 110 |
| abstract_inverted_index.various | 81 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 83 |
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| abstract_inverted_index.applying | 285 |
| abstract_inverted_index.averaged | 259 |
| abstract_inverted_index.commonly | 31 |
| abstract_inverted_index.estimate | 34, 129, 196 |
| abstract_inverted_index.improved | 220 |
| abstract_inverted_index.learning | 68, 99, 180, 287 |
| abstract_inverted_index.networks | 113 |
| abstract_inverted_index.pressure | 7 |
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| abstract_inverted_index.quantify | 290 |
| abstract_inverted_index.sampling | 60 |
| abstract_inverted_index.science. | 23 |
| abstract_inverted_index.sources. | 82 |
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| abstract_inverted_index.advantage | 307 |
| abstract_inverted_index.answering | 53 |
| abstract_inverted_index.automatic | 75, 299 |
| abstract_inverted_index.collected | 50, 169 |
| abstract_inverted_index.counting, | 91 |
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| abstract_inverted_index.exhibited | 253 |
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| abstract_inverted_index.potential | 72 |
| abstract_inverted_index.pressure. | 63 |
| abstract_inverted_index.questions | 56 |
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| abstract_inverted_index.simulated | 207 |
| abstract_inverted_index.technique | 127 |
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| abstract_inverted_index.HerbiEstim | 187 |
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| abstract_inverted_index.collection | 76 |
| abstract_inverted_index.ecological | 78 |
| abstract_inverted_index.efficiency | 222 |
| abstract_inverted_index.estimation | 103, 248, 300 |
| abstract_inverted_index.facilitate | 322 |
| abstract_inverted_index.generative | 111 |
| abstract_inverted_index.hands‐on | 162 |
| abstract_inverted_index.herbivores | 17 |
| abstract_inverted_index.intensity. | 293 |
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| corresponding_author_ids | https://openalex.org/A5001473679, https://openalex.org/A5100712588 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I159129438 |
| citation_normalized_percentile.value | 0.96727834 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |