Abstract
Background: In view of the existence of light shadow, branches occlusion, and leaves overlapping conditions in the real natural environment, problems such as slow detection speed, low detection accuracy, high missed detection rate, and poor robustness in plant diseases and pests detection technology arise.Results: Based on YOLOv3-tiny network architecture, to reduce layer-by-layer loss of information during network transmission, and to learn from the idea of inverse-residual block, this study proposes a YOLOv3-tiny-IRB algorithm to optimize its feature extraction network, improve the gradient disappearance phenomenon during network deepening, avoid feature information loss, and realize network multilayer feature multiplexing and fusion. The network is trained by the methods of expanding datasets and multiscale strategies to obtain the optimal weight model.Conclusion: The experimental results show that when the method is tested on the self-built tomato diseases and pests dataset, and while ensuring the detection speed (206 frame rate per second), the mean Average precision (mAP) under three conditions: (a) deep separation, (b) debris occlusion, and (c) leaves overlapping are 98.3, 92.1, and 90.2%, respectively. Compared with the current mainstream object detection methods, the proposed method improves the detection accuracy of tomato diseases and pests under conditions of occlusion and overlapping in real natural environment.
Highlights
Tomato is one of the most popular crops planted in China, and it has an irreplaceable position in vegetables, fruits, medicinal, and other aspects, with a huge planting volume and demand (Li, 2012)
Sample S is divided into four types according to the combination of the true category of sample S and the predicted category of model: True positive (TP) represents the number of correctly classified positive samples, FP represents the number of incorrectly classified positive samples, false negative (FN) represents the number of incorrectly classified negative samples (FN), and true negative (TN) represents the number of correctly classified negative samples
The detection accuracy of the model obtained by using the training set marked by the foreground region is significantly improved compared with the model without foreground region labeling, and the mean Average precision (mAP) and F1 score of all objects in the test set are improved by 1.4% and 0.024, respectively
Summary
Tomato is one of the most popular crops planted in China, and it has an irreplaceable position in vegetables, fruits, medicinal, and other aspects, with a huge planting volume and demand (Li, 2012). The products are exported to Russia, North Korea, Myanmar, and other countries. This town is an important tomato production and sales base in Shandong Province and enjoys the reputation of “small town with tomato characteristics.”. In view of the existence of light shadow, branches occlusion, and leaves overlapping conditions in the real natural environment, problems such as slow detection speed, low detection accuracy, high missed detection rate, and poor robustness in plant diseases and pests detection technology arise
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