Robust Image Segmentation Method for Cotton Leaf Under Natural Conditions Based on Immune Algorithm and PCNN Algorithm
Abstract
In the actual cotton planting environment, rapid change of light within a day, reflection from different backgrounds and different weather conditions can affect the imaging of cotton. Therefore, the crop object segmentation is difficult. Images which were captured in 12 natural scenes during cotton planting, including three weather conditions, such as sunny, cloudy and rainy and four soil cover conditions, such as white mulch film, black mulch film, straw and bare soil were regarded as the research objects. This paper presents the cotton leaf segmentation method based on Immune algorithm and pulse coupled neural networks (PCNN). First, 17 color components of white mulch film, black mulch film, straw, bare soil and cotton under the conditions of sunny, cloudy and rainy days were analyzed by using statistical method. Three high feasible and anti-light color components were selected by histogram statistical with mean gray value. Second, the optimal parameters of PCNN model and the optimal number of iterations were determined by using immune algorithm optimization theory, and the method in this paper was tested by using 1200 cotton images which were captured under 12 natural scenes. Finally, the test results showed that this method can distinguish cotton target region from soil and other background regions. Meanwhile, for reflection of mulch film, crop shadow, dark light, complex background, noise, etc. which are often appeared in natural scene, four image segmentation methods of Otsu algorithm, K-Means algorithm, FCM algorithm and PCNN were compared with the proposed method in this paper. The segmentation result showed that the proposed method has good resistance to change of light and complex background. The average ME of the proposed method is 6.5%, significantly lower than that of other four methods and the performance is better than other four methods. This method can segment cotton images in different weather conditions and different backgrounds accurately under complex natural conditions. It will contribute to the subsequent growth status determination and pest diagnosis of cotton.
