Yang Xinting, Liu Mengmeng, Xu Jianping, Zhao Li, Wei Shujun, Li Wenyong, Chen Meixiang, Chen Ming, Li Ming. Image segmentation and recognition algorithm of greenhouse whitefly and thrip adults for automatic monitoring device[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(1): 164-170. DOI: 10.11975/j.issn.1002-6819.2018.01.022
    Citation: Yang Xinting, Liu Mengmeng, Xu Jianping, Zhao Li, Wei Shujun, Li Wenyong, Chen Meixiang, Chen Ming, Li Ming. Image segmentation and recognition algorithm of greenhouse whitefly and thrip adults for automatic monitoring device[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(1): 164-170. DOI: 10.11975/j.issn.1002-6819.2018.01.022

    Image segmentation and recognition algorithm of greenhouse whitefly and thrip adults for automatic monitoring device

    • Abstract: Pests were one of the important factors for crop loss, so accurate counting and identification of insect were important for pest detection and preventive measures. To monitor the variety and quantity of cucumber pests in greenhouse and predict the development trend of pest, an image recognition algorithm based on Prewitt, Canny edge detection operator segmentation and support vector machine (SVM) was proposed for greenhouse whitefly and thrip on the trap board. In recent years, due to the improvement of computer hardware and imaging equipment, automatic recognition of pests based on image processing technology has been widely studied. The common counting and recognition methods were mainly based on computer vision, the pest target was segmented from the background image firstly, and then the pests were counted and identified. Classification and counting inevitably required precision control. Firstly, pest images were captured by field automatic pest monitoring camera equipment. It was necessary to divide the pest area from the image in order to extract the valid feature parameters. For the sake of raising the effect of edge detection, it was significant to strengthen the contrast between the target and the background. The method used the high contrast of the pest target and the background in the b component binary image of the L*a*b* color space and the I component binary image of the HSI (hue-saturation-intensity) color space, and used the Prewitt operator and the Canny operator to perform the single-headed pests edge segmentation respectively. After morphological processing, differential method was used to remove the interference and tiny hole filled, and the 2 binary images were merged to complete the extraction of single-headed pests. The method extracted 5 morphological features (area, relative area, perimeter, complexity, and duty ratio) of pests, and because of the great diversity of color in pests, 9 color features (the first moment of each component of HSV (hue-saturation-value) color space, HSI color space and L*a*b* color space) were extracted. And the 14 characteristic parameters were normalized, and used as the input vector of the SVM to identify the image of cucumber pests. The pest identification rates of BP (back propagation) and SVM were compared, and the pest identification rates of 4 SVM kernel functions were also compared. The results show that the color eigenvector is the principal component of pest identification and SVM is better than BP neural network. SVM classification of linear kernel function has the best classification performance and stability, the average recognition accuracy is 93.5%, and the pest identification rates of whitefly and thrip are 96.0% and 91.0% respectively. The proposed method can realize the image recognition of greenhouse sticky traps. The study can support the pest detection and early warning, and provide important basis for timely and correct control measures.
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