Improved Tomato Leaf Disease Recognition Based on the YOLOv5m with Various Soft Attention Module Combinations Article Swipe
Yong-Suk Lee
,
Maheshkumar Prakash Patil
,
Jeong Gyu Kim
,
Seong Seok Choi
,
Yong Bae Seo
,
Gun‐Do Kim
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture14091472
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture14091472
To reduce production costs, environmental effects, and crop losses, tomato leaf disease recognition must be accurate and fast. Early diagnosis and treatment are necessary to cure and control illnesses and ensure tomato output and quality. The YOLOv5m was improved by using C3NN modules and Bidirectional Feature Pyramid Network (BiFPN) architecture. The C3NN modules were designed by integrating several soft attention modules into the C3 module: the Convolutional Block Attention Module (CBAM), Squeeze and Excitation Network (SE), Efficient Channel Attention (ECA), and Coordinate Attention (CA). The C3 modules in the Backbone and Head of YOLOv5 model were replaced with the C3NN to improve feature representation and object detection accuracy. The BiFPN architecture was implemented in the Neck of the YOLOv5 model to effectively merge multi-scale features and improve the accuracy of object detection. Among the various combinations for the improved YOLOv5m model, the C3ECA-BiFPN-C3ECA-YOLOv5m achieved a precision (P) of 87.764%, a recall (R) of 87.201%, an F1 of 87.482, an mAP.5 of 90.401%, and an mAP.5:.95 of 68.803%. In comparison with the YOLOv5m and Faster-RCNN models, the improved models showed improvement in P by 1.36% and 7.80%, R by 4.99% and 5.51%, F1 by 3.18% and 6.86%, mAP.5 by 1.74% and 2.90%, and mAP.5:.95 by 3.26% and 4.84%, respectively. These results demonstrate that the improved models have effective tomato leaf disease recognition capabilities and are expected to contribute significantly to the development of plant disease detection technology.
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- en
- Landing Page
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- https://www.mdpi.com/2077-0472/14/9/1472/pdf?version=1724928886
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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Improved Tomato Leaf Disease Recognition Based on the YOLOv5m with Various Soft Attention Module CombinationsWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-08-29Full publication date if available
- Authors
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Yong-Suk Lee, Maheshkumar Prakash Patil, Jeong Gyu Kim, Seong Seok Choi, Yong Bae Seo, Gun‐Do KimList of authors in order
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https://doi.org/10.3390/agriculture14091472Publisher landing page
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https://www.mdpi.com/2077-0472/14/9/1472/pdf?version=1724928886Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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BiologyTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 9, 2024: 1Per-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/W3153257580, https://openalex.org/W3082289303, https://openalex.org/W4313570172, https://openalex.org/W2598645336, https://openalex.org/W4313529952, https://openalex.org/W4281264259, https://openalex.org/W2789255992, https://openalex.org/W4385495695, https://openalex.org/W2963037989, https://openalex.org/W2570343428, https://openalex.org/W4386076325, https://openalex.org/W3146366485, https://openalex.org/W2147990838, https://openalex.org/W3212386989, https://openalex.org/W2799244840, https://openalex.org/W1514535095, https://openalex.org/W6682137061, https://openalex.org/W2884585870, https://openalex.org/W2752782242, https://openalex.org/W3034552520, https://openalex.org/W3177052299, https://openalex.org/W4390884570, https://openalex.org/W4384929674, https://openalex.org/W4212965978, https://openalex.org/W4210261474, https://openalex.org/W4390743321, https://openalex.org/W4391385012, https://openalex.org/W4321014256, https://openalex.org/W4393164388, https://openalex.org/W4296058637, https://openalex.org/W4401415788, https://openalex.org/W4393317308, https://openalex.org/W4390876195, https://openalex.org/W4380870128, https://openalex.org/W3042011474, https://openalex.org/W4382297414, https://openalex.org/W2565639579, https://openalex.org/W2963857746, https://openalex.org/W2194775991, https://openalex.org/W2613718673, https://openalex.org/W2144506857, https://openalex.org/W2962766617, https://openalex.org/W6752378368, https://openalex.org/W4386090585, https://openalex.org/W4304128598, https://openalex.org/W4392249012, https://openalex.org/W4379472202, https://openalex.org/W4380683841, https://openalex.org/W3034971973, https://openalex.org/W2073056556, https://openalex.org/W2009974983, https://openalex.org/W2963284197, https://openalex.org/W2514087538, https://openalex.org/W3102927640, https://openalex.org/W4384694932, https://openalex.org/W6857073320, https://openalex.org/W4387903258 |
| referenced_works_count | 57 |
| abstract_inverted_index.P | 181 |
| abstract_inverted_index.R | 186 |
| abstract_inverted_index.a | 144, 149 |
| abstract_inverted_index.C3 | 63, 85 |
| abstract_inverted_index.F1 | 155, 191 |
| abstract_inverted_index.In | 167 |
| abstract_inverted_index.To | 0 |
| abstract_inverted_index.an | 154, 158, 163 |
| abstract_inverted_index.be | 14 |
| abstract_inverted_index.by | 39, 55, 182, 187, 192, 197, 203 |
| abstract_inverted_index.in | 87, 113, 180 |
| abstract_inverted_index.of | 92, 116, 129, 147, 152, 156, 160, 165, 231 |
| abstract_inverted_index.to | 24, 100, 120, 225, 228 |
| abstract_inverted_index.(P) | 146 |
| abstract_inverted_index.(R) | 151 |
| abstract_inverted_index.The | 35, 50, 84, 108 |
| abstract_inverted_index.and | 6, 16, 20, 26, 29, 33, 43, 72, 80, 90, 104, 125, 162, 172, 184, 189, 194, 199, 201, 205, 222 |
| abstract_inverted_index.are | 22, 223 |
| abstract_inverted_index.for | 136 |
| abstract_inverted_index.the | 62, 65, 88, 98, 114, 117, 127, 133, 137, 141, 170, 175, 212, 229 |
| abstract_inverted_index.was | 37, 111 |
| abstract_inverted_index.C3NN | 41, 51, 99 |
| abstract_inverted_index.Head | 91 |
| abstract_inverted_index.Neck | 115 |
| abstract_inverted_index.crop | 7 |
| abstract_inverted_index.cure | 25 |
| abstract_inverted_index.have | 215 |
| abstract_inverted_index.into | 61 |
| abstract_inverted_index.leaf | 10, 218 |
| abstract_inverted_index.must | 13 |
| abstract_inverted_index.soft | 58 |
| abstract_inverted_index.that | 211 |
| abstract_inverted_index.were | 53, 95 |
| abstract_inverted_index.with | 97, 169 |
| abstract_inverted_index.(CA). | 83 |
| abstract_inverted_index.(SE), | 75 |
| abstract_inverted_index.1.36% | 183 |
| abstract_inverted_index.1.74% | 198 |
| abstract_inverted_index.3.18% | 193 |
| abstract_inverted_index.3.26% | 204 |
| abstract_inverted_index.4.99% | 188 |
| abstract_inverted_index.Among | 132 |
| abstract_inverted_index.BiFPN | 109 |
| abstract_inverted_index.Block | 67 |
| abstract_inverted_index.Early | 18 |
| abstract_inverted_index.These | 208 |
| abstract_inverted_index.fast. | 17 |
| abstract_inverted_index.mAP.5 | 159, 196 |
| abstract_inverted_index.merge | 122 |
| abstract_inverted_index.model | 94, 119 |
| abstract_inverted_index.plant | 232 |
| abstract_inverted_index.using | 40 |
| abstract_inverted_index.(ECA), | 79 |
| abstract_inverted_index.2.90%, | 200 |
| abstract_inverted_index.4.84%, | 206 |
| abstract_inverted_index.5.51%, | 190 |
| abstract_inverted_index.6.86%, | 195 |
| abstract_inverted_index.7.80%, | 185 |
| abstract_inverted_index.Module | 69 |
| abstract_inverted_index.YOLOv5 | 93, 118 |
| abstract_inverted_index.costs, | 3 |
| abstract_inverted_index.ensure | 30 |
| abstract_inverted_index.model, | 140 |
| abstract_inverted_index.models | 177, 214 |
| abstract_inverted_index.object | 105, 130 |
| abstract_inverted_index.output | 32 |
| abstract_inverted_index.recall | 150 |
| abstract_inverted_index.reduce | 1 |
| abstract_inverted_index.showed | 178 |
| abstract_inverted_index.tomato | 9, 31, 217 |
| abstract_inverted_index.(BiFPN) | 48 |
| abstract_inverted_index.(CBAM), | 70 |
| abstract_inverted_index.87.482, | 157 |
| abstract_inverted_index.Channel | 77 |
| abstract_inverted_index.Feature | 45 |
| abstract_inverted_index.Network | 47, 74 |
| abstract_inverted_index.Pyramid | 46 |
| abstract_inverted_index.Squeeze | 71 |
| abstract_inverted_index.YOLOv5m | 36, 139, 171 |
| abstract_inverted_index.control | 27 |
| abstract_inverted_index.disease | 11, 219, 233 |
| abstract_inverted_index.feature | 102 |
| abstract_inverted_index.improve | 101, 126 |
| abstract_inverted_index.losses, | 8 |
| abstract_inverted_index.models, | 174 |
| abstract_inverted_index.module: | 64 |
| abstract_inverted_index.modules | 42, 52, 60, 86 |
| abstract_inverted_index.results | 209 |
| abstract_inverted_index.several | 57 |
| abstract_inverted_index.various | 134 |
| abstract_inverted_index.68.803%. | 166 |
| abstract_inverted_index.87.201%, | 153 |
| abstract_inverted_index.87.764%, | 148 |
| abstract_inverted_index.90.401%, | 161 |
| abstract_inverted_index.Backbone | 89 |
| abstract_inverted_index.accuracy | 128 |
| abstract_inverted_index.accurate | 15 |
| abstract_inverted_index.achieved | 143 |
| abstract_inverted_index.designed | 54 |
| abstract_inverted_index.effects, | 5 |
| abstract_inverted_index.expected | 224 |
| abstract_inverted_index.features | 124 |
| abstract_inverted_index.improved | 38, 138, 176, 213 |
| abstract_inverted_index.quality. | 34 |
| abstract_inverted_index.replaced | 96 |
| abstract_inverted_index.Attention | 68, 78, 82 |
| abstract_inverted_index.Efficient | 76 |
| abstract_inverted_index.accuracy. | 107 |
| abstract_inverted_index.attention | 59 |
| abstract_inverted_index.detection | 106, 234 |
| abstract_inverted_index.diagnosis | 19 |
| abstract_inverted_index.effective | 216 |
| abstract_inverted_index.illnesses | 28 |
| abstract_inverted_index.mAP.5:.95 | 164, 202 |
| abstract_inverted_index.necessary | 23 |
| abstract_inverted_index.precision | 145 |
| abstract_inverted_index.treatment | 21 |
| abstract_inverted_index.Coordinate | 81 |
| abstract_inverted_index.Excitation | 73 |
| abstract_inverted_index.comparison | 168 |
| abstract_inverted_index.contribute | 226 |
| abstract_inverted_index.detection. | 131 |
| abstract_inverted_index.production | 2 |
| abstract_inverted_index.Faster-RCNN | 173 |
| abstract_inverted_index.demonstrate | 210 |
| abstract_inverted_index.development | 230 |
| abstract_inverted_index.effectively | 121 |
| abstract_inverted_index.implemented | 112 |
| abstract_inverted_index.improvement | 179 |
| abstract_inverted_index.integrating | 56 |
| abstract_inverted_index.multi-scale | 123 |
| abstract_inverted_index.recognition | 12, 220 |
| abstract_inverted_index.technology. | 235 |
| abstract_inverted_index.architecture | 110 |
| abstract_inverted_index.capabilities | 221 |
| abstract_inverted_index.combinations | 135 |
| abstract_inverted_index.Bidirectional | 44 |
| abstract_inverted_index.Convolutional | 66 |
| abstract_inverted_index.architecture. | 49 |
| abstract_inverted_index.environmental | 4 |
| abstract_inverted_index.respectively. | 207 |
| abstract_inverted_index.significantly | 227 |
| abstract_inverted_index.representation | 103 |
| abstract_inverted_index.C3ECA-BiFPN-C3ECA-YOLOv5m | 142 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5069548951 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I8991828 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/12 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Responsible consumption and production |
| citation_normalized_percentile.value | 0.97243313 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |