Automatic detection and counting of Tuta absoluta (Myrick) using deep learning technique | ||
| آفات و بیماری های گیاهی | ||
| Volume 91, Issue 2 - Serial Number 117, 2024, Pages 149-156 PDF (459.49 K) | ||
| Document Type: Pest Management | ||
| DOI: 10.22092/jaep.2023.362402.1481 | ||
| Authors | ||
| Alireza Shabaninejad1; Abbas Ali Zamani* 2; Majid Iranpour3; Saeed Abbasi4; Faranak Ranjbar5 | ||
| 1Department of Plant Protection, College of Agriculture, Razi University | ||
| 2Department of Plant Protection, Faculty of Agriculture, Razi University, Kermanshah | ||
| 3College of Computer Engineering and IT, Payam Noor Mobarakeh University, Isfahan, Iran | ||
| 4Department of Plant Protection, College of Agriculture, Razi University, Kermanshah | ||
| 5Department of Soil Science, College of Agriculture, Razi University, Kermanshah | ||
| Abstract | ||
| The use of modern technologies for detecting and measuring pest population density can be an important step in facilitating the implementation of integrated pest management programs and achieving more precise and effective control. In this study, the deep learning technique and convolutional neural network with AlexNet architecture were used for the automatic detection and counting of the tomato leaf miner, Tuta absoluta (Myrick) (Lepidoptera: Gelechiidae), which is one of the key pests of tomato plants in Iran. To collect images of adult T. absoluta insects, 15 delta traps were installed in two hectares of tomato farms at the Campus of Agricultural and Natural Resources, Razi University. The Sony DSC-WX100 camera with an effective sensor resolution of 18 megapixels was used to capture the images. The performance of the convolutional neural network with the AlexNet architecture was evaluated using the parameters of average accuracy, accuracy, and recall. For counting performance, the linear regression curve and coefficient of determination were used. The average accuracy (98.0), accuracy (100), and recall (100) parameters indicate the high performance of the convolutional neural network in detecting the tomato leaf miner, and the coefficient of determination (0.98) indicates the network's high accuracy in counting this pest. Overall, the results demonstrate that the neural network can provide a practical solution for the accurate detection and counting of this pest on tomato plants using captured images | ||
| Keywords | ||
| AlexNet architecture; deep learning; tomato leaf miner | ||
| References | ||
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