Daejeon Real-time Pest Detection Device And Its Image Pre-processing Technology

Nowadays, China is pushing IPM greatly in agriculture. One aim of project is to decrease the amount of lost resulted from pest by using various ways. Another is to reduce the use of pesticide in environment as possible in order to ensure and even strengthen the sustainable development of agriculture. Therefore, insect pest detection and prediction of agriculture are important part of IPM project. It is the groundwork of pest control services at all levels too.At the present time the prediction method of attracting pest with black light and recognizing and counting by man is adopted generally. There are some serious shortages such as bad recognition accuracy and low efficiency. It reduces seriously accuracy and timeliness of prediction and is disadvantage in guiding insect disease prevention. Therefore, this paper researched image recognition technology based on image processing. This technology will automatically detect and predict the amount and sort of various insect pests.We designed and made the attraction device to attract agricultural pests, obtained agricultural pests' images with the color camera and processed images . On the basis of these, we emphasized on extracting effective features based on the theory of Mathematics morphology. Then we selected features, inputted into neural network classifier, recognition, presented detection results.For this research object, pest of the field in agriculture, the paper carry on the following work:1. To design the attraction and transit device. We attracted insect pests with black light which was used by pest control services, adjusting insect pests' posture by running water to ensure a steady transit.2. To offer equality and invariable lamp. Using the theory of shadowless lamp, we made even illumination room guarantee illumination quality to obtain the image through adopting symmetrical light source and diffuse reflection material.3. To set image collection mode by programming based on the OK-C30S image card API. It realized parallel running of the image recognition system.4. The processing of pests' image. In this tech-develop stage, We fulfilled images enhancement The grain-pests image is smoothed by the gray morphology, enhanced by the adaptive method. Eventually, we segmented the object images form theirbackground.5. Feature extracting and selecting. It is the crucial part of the system. What features we selected are as follow: area, perimeter, complexity, equivalent circle radius, and so on. Then, we made use of K-L translation based on the global in-class discrete matrix and the distance separable rule to realize the features' compressing.6. Image recognition and classifier design.We implemented mentioned above functions with Visual C++6.0 language, developed software package, associated with designed hardware system.Due to my poor tech and other confining of some objective condition, there are still a lot of shortcomings in design of hardware and software. However, I firmly believe that a desirable future in this field lies open to us.