Automatic Recognition for Cotton Spider Mites Damage Level Based on SVM and AdaBoost

YANG Lili, ZHANG Dawei, LUO Jun, WANG Zhenpeng, WU Caicong

Abstract

Aiming at the difficulty in identifying the level of cotton spider mites under natural conditions, an automatic identification method was proposed for rapid detection of cotton spider mites damage under natural conditions. The damaged cotton leaves images collected by mobile phone under natural conditions were used as the object. Firstly, the Otsu method and the regional interconnection marking algorithm were used to separate image of cotton leaf from background. Then, the authors combined the colors, textures, and edge features of the image of damaged cotton spider mites. The support vector machine (SVM) was used to classify the data separately. The average recognition rate of 76.25% was obtained. Finally, it was tried to recognize the mode based on combining the SVM and AdaBoost algorithm to classify the cotton spider mites hazard criteria. With this mode, the average recognition accuracy rate finally reached 88.75%, which was 13.75 percentage points higher than that of BP neural network, 12.5 percentage points higher than that of the SVM algorithm alone and 8.75 percentage points higher than that of the AdaBoost algorithm alone with comparative experiments. In conclusion, it was fully proved that the identification method mentioned can be used to better identify the cotton spider mites damage level, which provided data support for the digital control of cotton spider mites and variable spraying.


Keywords: cotton, cotton spider mites, damage level, support vector machine, AdaBoost

 

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