Abstract
Combining with deep learning technology, this paper proposes a method of farmland pest recognition based on target detection algorithm, which realizes the automatic recognition of farmland pest and improves the recognition accuracy. First of all, a labeled farm pest database is established; then uses Faster R-CNN algorithm, the model uses the improved Inception network for testing; finally, the proposed target detection model is trained and tested on the farm pest database, with the average precision up to 90.54%.
Highlights
For a large agricultural country, the production status of rice, wheat, corn, soybean and other crops plays an important role in the stable development of the national economy
In order to achieve more effective and wider application of farmland pest detection technology, this paper combines deep learning and field pest detection, and proposes a field pest identification method based on target detection algorithm, which greatly improves the accuracy of field pest detection
Experimental results When using the farm pest database detection model to train, in order to ensure that all samples can be used for training and testing, this paper uses K-fold cross-validation[15], where K is selected 10 and 9 subsets are selected for training Data, 1 subset as test data
Summary
For a large agricultural country, the production status of rice, wheat, corn, soybean and other crops plays an important role in the stable development of the national economy. Agricultural production is still affected by the environment, Climate change, environmental pollution, agricultural disasters and improper management and other factors will cause crop yield reduction, especially the impact of pests in farmland is more serious. In this case alone, the annual agricultural economic losses in Europe reach 28.2%, North America reaches 31.2%, and the economic losses in Asia and Africa are as high as 50%[1] .Traditional pest identification and diagnosis methods mainly rely on manual identification[2], which is closely related to the comprehensive quality of professionals. In order to achieve more effective and wider application of farmland pest detection technology, this paper combines deep learning and field pest detection, and proposes a field pest identification method based on target detection algorithm, which greatly improves the accuracy of field pest detection
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