Research Article
Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques
| ā | Detection time1 (s) | Faster R-CNN | Mask R-CNN | Diseases | VGG-16 | ResNet-50 | ResNet-101 | MobileNet | ResNet-50 | ResNet-101 |
| Healthy tomato | 0.158 | 0.160 | 0.174 | 0.088 | 0.166 | 0.186 | Tomato malformed fruit | 0.164 | 0.177 | 0.192 | 0.103 | 0.189 | 0.200 | Tomato blotchy ripening | 0.234 | 0.242 | 0.268 | 0.143 | 0.261 | 0.279 | Tomato puffy fruit | 0.209 | 0.226 | 0.243 | 0.164 | 0.236 | 0.253 | Tomato dehiscent fruit | 0.165 | 0.173 | 0.193 | 0.109 | 0.184 | 0.204 | Tomato blossom-end rot | 0.238 | 0.240 | 0.264 | 0.143 | 0.250 | 0.275 | Tomato sunscald | 0.233 | 0.262 | 0.291 | 0.161 | 0.282 | 0.311 | Tomato virus disease | 0.205 | 0.213 | 0.222 | 0.114 | 0.222 | 0.232 | Tomato gray mold | 0.218 | 0.227 | 0.231 | 0.123 | 0.238 | 0.242 | Tomato ulcer disease | 0.318 | 0.326 | 0.365 | 0.220 | 0.345 | 0.377 | Tomato anthracnose | 0.411 | 0.490 | 0.547 | 0.212 | 0.585 | 0.667 | Mean time | 0.202 | 0.209 | 0.226 | 0.123 | 0.227 | 0.246 |
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1The detection time of the model on each tomato disease types; bold face indicates the minimum detection time of the model.
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