Automatic greenhouse pest recognition based on multiple color space features Article Swipe
Zhankui Yang
,
Wenyong Li
,
Ming Li
,
Xinting Yang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.25165/ijabe.v14i2.5098
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.25165/ijabe.v14i2.5098
Recognition and counting of greenhouse pests are important for monitoring and forecasting pest population dynamics. This study used image processing techniques to recognize and count whiteflies and thrips on a sticky trap located in a greenhouse environment. The digital images of sticky traps were collected using an image-acquisition system under different greenhouse conditions. If a single color space is used, it is difficult to segment the small pests correctly because of the detrimental effects of non-uniform illumination in complex scenarios. Therefore, a method that first segments object pests in two color spaces using the Prewitt operator in I component of the hue-saturation-intensity (HSI) color space and the Canny operator in the B component of the Lab color space was proposed. Then, the segmented results for the two-color spaces were summed and achieved 91.57% segmentation accuracy. Next, because different features of pests contribute differently to the classification of pest species, the study extracted multiple features (e.g., color and shape features) in different color spaces for each segmented pest region to improve the recognition performance. Twenty decision trees were used to form a strong ensemble learning classifier that used a majority voting mechanism and obtains 95.73% recognition accuracy. The proposed method is a feasible and effective way to process greenhouse pest images. The system accurately recognized and counted pests in sticky trap images captured under real greenhouse conditions. Keywords: ensemble learning classifier, greenhouse sticky trap, automated pest recognition and counting, HSI and Lab color spaces, multiple color space features DOI: 10.25165/j.ijabe.20211402.5098 Citation: Yang Z K, Li W Y, Li M, Yang X T. Automatic greenhouse pest recognition based on multiple color space features. Int J Agric & Biol Eng, 2021; 14(2): 188–195.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://www.abepublishing.org/journals/index.php/ijabe/article/download/5098/pdf
- OA Status
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W3151126183Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.25165/ijabe.v14i2.5098Digital Object Identifier
- Title
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Automatic greenhouse pest recognition based on multiple color space featuresWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-04-03Full publication date if available
- Authors
-
Zhankui Yang, Wenyong Li, Ming Li, Xinting YangList of authors in order
- Landing page
-
https://www.abepublishing.org/journals/index.php/ijabe/article/download/5098/pdfPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.abepublishing.org/journals/index.php/ijabe/article/download/5098/pdfDirect OA link when available
- Concepts
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Artificial intelligence, Computer vision, Color space, Hue, Computer science, Pattern recognition (psychology), Greenhouse, Prewitt operator, PEST analysis, Image processing, Mathematics, Edge detection, Biology, Agronomy, Image (mathematics), BotanyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(HSI) | 106 |
| abstract_inverted_index.2021; | 285 |
| abstract_inverted_index.Agric | 281 |
| abstract_inverted_index.Canny | 111 |
| abstract_inverted_index.Next, | 141 |
| abstract_inverted_index.Then, | 125 |
| abstract_inverted_index.based | 272 |
| abstract_inverted_index.color | 59, 94, 107, 120, 161, 167, 249, 252, 275 |
| abstract_inverted_index.count | 25 |
| abstract_inverted_index.first | 88 |
| abstract_inverted_index.image | 19 |
| abstract_inverted_index.pests | 5, 70, 91, 146, 225 |
| abstract_inverted_index.shape | 163 |
| abstract_inverted_index.small | 69 |
| abstract_inverted_index.space | 60, 108, 121, 253, 276 |
| abstract_inverted_index.study | 17, 156 |
| abstract_inverted_index.trap, | 240 |
| abstract_inverted_index.traps | 44 |
| abstract_inverted_index.trees | 182 |
| abstract_inverted_index.under | 51, 231 |
| abstract_inverted_index.used, | 62 |
| abstract_inverted_index.using | 47, 96 |
| abstract_inverted_index.(e.g., | 160 |
| abstract_inverted_index.14(2): | 286 |
| abstract_inverted_index.91.57% | 137 |
| abstract_inverted_index.95.73% | 200 |
| abstract_inverted_index.Twenty | 180 |
| abstract_inverted_index.images | 41, 229 |
| abstract_inverted_index.method | 86, 206 |
| abstract_inverted_index.object | 90 |
| abstract_inverted_index.region | 173 |
| abstract_inverted_index.single | 58 |
| abstract_inverted_index.spaces | 95, 132, 168 |
| abstract_inverted_index.sticky | 31, 43, 227, 239 |
| abstract_inverted_index.strong | 188 |
| abstract_inverted_index.summed | 134 |
| abstract_inverted_index.system | 50, 220 |
| abstract_inverted_index.thrips | 28 |
| abstract_inverted_index.voting | 196 |
| abstract_inverted_index.Prewitt | 98 |
| abstract_inverted_index.because | 72, 142 |
| abstract_inverted_index.complex | 81 |
| abstract_inverted_index.counted | 224 |
| abstract_inverted_index.digital | 40 |
| abstract_inverted_index.effects | 76 |
| abstract_inverted_index.images. | 217 |
| abstract_inverted_index.improve | 175 |
| abstract_inverted_index.located | 33 |
| abstract_inverted_index.obtains | 199 |
| abstract_inverted_index.process | 214 |
| abstract_inverted_index.results | 128 |
| abstract_inverted_index.segment | 67 |
| abstract_inverted_index.spaces, | 250 |
| abstract_inverted_index.achieved | 136 |
| abstract_inverted_index.captured | 230 |
| abstract_inverted_index.counting | 2 |
| abstract_inverted_index.decision | 181 |
| abstract_inverted_index.ensemble | 189, 235 |
| abstract_inverted_index.feasible | 209 |
| abstract_inverted_index.features | 144, 159 |
| abstract_inverted_index.learning | 190, 236 |
| abstract_inverted_index.majority | 195 |
| abstract_inverted_index.multiple | 158, 251, 274 |
| abstract_inverted_index.operator | 99, 112 |
| abstract_inverted_index.proposed | 205 |
| abstract_inverted_index.segments | 89 |
| abstract_inverted_index.species, | 154 |
| abstract_inverted_index.Automatic | 268 |
| abstract_inverted_index.accuracy. | 139, 202 |
| abstract_inverted_index.automated | 241 |
| abstract_inverted_index.collected | 46 |
| abstract_inverted_index.component | 102, 116 |
| abstract_inverted_index.correctly | 71 |
| abstract_inverted_index.counting, | 245 |
| abstract_inverted_index.different | 52, 143, 166 |
| abstract_inverted_index.difficult | 65 |
| abstract_inverted_index.dynamics. | 14 |
| abstract_inverted_index.effective | 211 |
| abstract_inverted_index.extracted | 157 |
| abstract_inverted_index.features) | 164 |
| abstract_inverted_index.features. | 277 |
| abstract_inverted_index.important | 7 |
| abstract_inverted_index.mechanism | 197 |
| abstract_inverted_index.proposed. | 123 |
| abstract_inverted_index.recognize | 23 |
| abstract_inverted_index.segmented | 127, 171 |
| abstract_inverted_index.two-color | 131 |
| abstract_inverted_index.188–195. | 287 |
| abstract_inverted_index.Therefore, | 84 |
| abstract_inverted_index.accurately | 221 |
| abstract_inverted_index.classifier | 191 |
| abstract_inverted_index.contribute | 147 |
| abstract_inverted_index.greenhouse | 4, 36, 53, 215, 233, 238, 269 |
| abstract_inverted_index.monitoring | 9 |
| abstract_inverted_index.population | 13 |
| abstract_inverted_index.processing | 20 |
| abstract_inverted_index.recognized | 222 |
| abstract_inverted_index.scenarios. | 82 |
| abstract_inverted_index.techniques | 21 |
| abstract_inverted_index.whiteflies | 26 |
| abstract_inverted_index.Recognition | 0 |
| abstract_inverted_index.classifier, | 237 |
| abstract_inverted_index.conditions. | 54 |
| abstract_inverted_index.detrimental | 75 |
| abstract_inverted_index.differently | 148 |
| abstract_inverted_index.forecasting | 11 |
| abstract_inverted_index.non-uniform | 78 |
| abstract_inverted_index.recognition | 177, 201, 243, 271 |
| abstract_inverted_index.environment. | 37 |
| abstract_inverted_index.illumination | 79 |
| abstract_inverted_index.performance. | 178 |
| abstract_inverted_index.segmentation | 138 |
| abstract_inverted_index.classification | 151 |
| abstract_inverted_index.features DOI: | 254 |
| abstract_inverted_index.image-acquisition | 49 |
| abstract_inverted_index.conditions. Keywords: | 234 |
| abstract_inverted_index.hue-saturation-intensity | 105 |
| abstract_inverted_index.10.25165/j.ijabe.20211402.5098 Citation: | 255 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.02155259 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |