Abstract
Crop disease and insect pest detection and recognition using machine vision can provide precise diagnosis and preventive suggestions. However, the complexity of agricultural pest and disease identification based on traditional bag of words (BOW) models is high and the effect is general. This paper presents a histogram quadric segmentation algorithm based on an evolutionary algorithm to observe the features (colour, texture) of disease spots and to learn from the guided filtering algorithm. This process aims to obtain the precise positions of disease spots in images. Dense-SIFT, which can extract features and spatial pyramid, which can map image features to high-spatial-resolution space, are simultaneously applied in the recognition of crop diseases and insect pests in the BOW model. The experimental results show that the new segmentation algorithm can effectively locate the positions of disease spots in corn images and the improved BOW model substantially increases the recognition accuracy of crop diseases and insect pests.
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