Three dimensional visual simulation method of crop disease state based on image

Abstract: Simulation of three-dimensional (3D) crop scene infected by crop disease is a tough task, because the related appearance data information is difficult to obtain. To obtain specific disease appearance information, careful bacteria culture and continuous observation may be needed with long-time experimental work and precise environmental control. This paper presents a general method to simulate the appearance transition of crop leaves infected by common diseases based on existing image in the Internet. We assume that a disease image contains some key appearance information in the process of disease infection. Based on this assumption, a set of static properties are extracted from image including shape and color of disease spots on the crop surface, and meanwhile the relevant dynamic transition processes of these properties are also deduced. For analyzing color transition, K-MEANS is firstly used to classify the color vectors of pixels in disease image into 8 categories and the average color vector of each category is computed which is called disease color feature vector. Then, these 8 vectors are sorted based on their proportions of green channel. To get a continual color aging simulation result, 7 linear functions are generated by interpolation between adjacent vectors. Finally, 141 discrete color vectors are sampled from these functions and used to generate the disease color transition texture. In order to obtain dynamic morphogenesis process of disease spot, the threshold segmentation method is firstly applied to segment the disease spot pixels from the pixels of normal crop leaves. Then a gray value is computed for each disease spot pixel based on the mimimum Euclidean distance between pixel's color vector and each disease color feature vector. These gray values of each disease spot pixel are recorded into the texture called morphogenesis texture. The distribution of disease spot on the crop organ surface is complex and random. A interactive interface tool has been developed for designing the distribution. With the tool, users can put some morphogenesis textures onto any location of the crop 3D models and change the size and direction of morphogenesis textures according to users' experience. The operating result is also saved as the texture called distribution texture. The disease color transition texture and distribution texture contain the necessary dynamic appearance information of disease spot and are used in the visualization step. For simulating a dynamic and continual appearance transition process of crop disease, a group of degree parameters for arbitrary 3D position on the crop surface are applied to generate the disease appearance which is computed using the distribution texture and the interactive parameter called general disease degree parameter. With the general degree parameter, user can get a simulation result under any infected state. In order to better define the disease appearance, we decompose it into the symptom appearance for describing the ageing status of the crop organ and the mildew layer appearance caused by the accumulation of mycelium. We consider the crop organ as a homogeneous structure and use the isotropic ward BRDF (bidirectional reflectance distribution function) model to simulate the symptom appearance. The diffuse reflection of ward model at arbitrary position on crop is selected from the color transition texture based on the degree parameter of this 3D position. In order to simulate the volumetric nature of the mildew layers, the shell model is integrated into our approach and the attributes of shell model are all controlled by the degree parameter. We have realized the algorithm in this paper using OpenGL, and found that the method can realistically render the appearance of the crop infected by the disease using only one or a few images. Our strategy is to use existing disease image from Internet to generate plant disease 3D animation, and it can solve the problem of the lack of related apparent data information of plant diseases. This research can provide a powerful tool to produce animations for agricultural science training.