Study On Wheat Aphids' Insect Pests Automatic Detection Techenology Based On Machine Vision

Wheat is one of the important crops of three in the world. Insect pests of wheat can reducethe output per acre. Aphids happen in our country were 2 hundreds million acres in 2005, and thered spider was 1 hundred million acres. The Attracts the thick liquid insect was 30000 thousandsacres. Those influence the output of wheat seriously.Effective insect pests monitoring is an important way to enhance the output of wheat.Monitoring and prevention can reduce the rate of insect pest's occur and destruction. Thusproduction cost would be reduced and efficiency would be enhanced during the production.Precise Spraying is a hot topic which many researchers studying it in precision agricultureand in a start stage domestic and overseas. Today, at the stage of insect pests' automatic detection,all studies were in specific and sole back ground and based on static stage images. Those methodswere far away of real-time detection. This topic was proposed based on 863 plan of China. In thistopic, the author studied thoroughly on automatic detection technology based on machine vision.1. Hypothesis that the object is young wheat insect in non-specific conditions, its haveCharacteristic include that small, color Contrast gradient, and protecting color which is near tobackground; its background are leaves and it is not consistent to processing conditions; A lot ofaphids' images were collected in natural light and prepared for goes a step of further experimentsand research.2. It elaborates the theory foundation of machine vision, image identification and imagesegment, analysis all kinds of images' features and making decision of texture as foundation ofimage classification; at the ground of thoroughly theory research, developing the algorithmsuitable this paper. And introducing theory foundations of Support Vector Machine, RegionGrowth, and k-means algorithm and so on to classify and segment images of insect pests, buildingthe foundation for research of algorithms.3. Algorithms of Image capturing, classification, segmentation and pattern processing werestudied respectively. And algorithm was developed suitable for insect pests' automatic detection innon-specific (undefined light and conditions). Paper finished dynamic images captured andprocessed, faster pro-processing, image segment and last processing, which prepared for goes astep of further in real -time insect pests' recognition in field.4. Aphids sample characteristics against, it researched the region growth, the Support VectorMachine segmentation and regional growth combined with vector machine segmentation approach,through comparing and testing, final separation methods were selected, and get an ideal segmentation results;5. Comparison the advantages and disadvantages of the proposed classification andsegmentation algorithm, and determine the method of classification and segmentation which cansegment images effectively and its speed can meet the requirements;6. The methods of Image marker and automatic counting the number were proposed. Thenumber of pests can be used for providing accurate data to support machinery application andgiving data support for decision spraying.7. A visual inspection system model was established, at last, this model was tested inlaboratory, and testing results confirmed validity and Reliability of this model;In summary, paper through theory analysis, algorithm model and theory combinedexperiments finished the algorithm and model's development of insect pests' automaticdetection,and this result can be used for goes a step of further research variable spraying and hasimportant instrumental and actual meanings for decision work.