Document Type : Pest Management
Authors
1 Razi University
2 Department of Plant Protection, Faculty of Agriculture, Razi University, Kermanshah
3 College of Computer Engineering and IT, Payam Noor Mobarakeh University, Isfahan, Iran
4 Department of Plant Protection, College of Agriculture, Razi University, Kermanshah
5 Department of Soil Science, College of Agriculture, Razi University, Kermanshah
Abstract
Accurately detecting and counting pests is crucial in pest management programs. Conventional methods of pest identification and counting on host plants are expensive, time-consuming, and subject to uncontrollable human errors. 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, 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 Campus of Agricultural and Natural Resources. 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.
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