Abstract：This paper studied a bamboo pests identification model based on artificial intelligence deep learning under the ecological context. An insect dataset with 5 663 photos was established, consisting of 3 species of bamboo pests and 3 other insect species. Furthermore, a special "network in network" architecture composed of Inception modules in a deep learning model named GoogLeNet was introduced and applied to extract image features. Meanwhile, this model was run on 4 train-test set split experiments. The experimental results showed that the accuracy of this model increased with the increase of the ratio of the training set. When the ratio of the train-test set was set to 9:1, this model achieved the best performance, and when the mean-F1-Score reached 95.48%, the overall classification accuracy reached 97.5%. These experimental results implied that this model possessed relatively good comprehensive performance and practicability. This model could realize the intelligent recognition of the 3 species of bamboo pests mentioned above under the ecological context and is an intelligent solution to the pest control in bamboo production and management. Additionally, it could provide effective technology backup for intensive and effective management of bamboo industry.
李禹辰, 李非非, 李见辉, 余飞, 徐杰. 生态背景下基于人工智能深度学习的竹类害虫识别方法研究[J]. 世界竹藤通讯, 2019, 17(3): 16-21.
Li Yuchen, Li Feifei, Li Jianhui, Yu Fei, Xu Jie. Research on Bamboo Pests Identification Method Based on Artificial Intelligence and Deep Learning under the Ecological Context. World Bamboo and Rattan, 2019, 17(3): 16-21.
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