Winter Wheat Growth Condition Diagnosis And Software Development Based On Image Processing Technology

Although the results of traditional chemical determination methods are reliable and accurate,they are time-consuming,complex and high cost.As a new analysis method,digital image processing technology has the characteristics of fast,low cost and popularity.However,the method of color threshold segmentation based on HSV color space is not mature,and t he current market lacks an easy to operate and popular winter wheat growth index diagnosis software,so this research is carried out.In this paper,in order to increase the number of data samples,the winter wheat of nitrogen operation research experiment and drought stress experiment was taken as the research object.By using the camera to shoot the winter wheat canopy from multiple angles,the multi angle image samples of winter wheat canopy were obtained.The openCV image processing technology was used to preprocess the canopy image,and the color characteristic parameters were extracted to find out the best correlation with the growth index Color characteristic parameters,using multiple linear regression,stepwise multiple linear regression,BP neural network to establish color characteristic parameters and growth index diagnosis model,combined with eclipse programming software and Java language to compile the optimal model into the software platform,so that it can process the winter wheat canopy image to retrieve its growth index state.The main conclusions are as follows1)Different shooting angles cause interference to the acquisition of canopy image.The canopy image of 60° has the best imaging effect,the best restoration,the highest accuracy o f modeling effect,the best effect,followed by 30° and the worst 90°.2)The five color parameters of R,G,B,reG and lnG in RGB color space had the best correlation with the growth indexes of winter wheat,such as leaf nitrogen content,chlorophyll content and leaf water content,and the R~2 coefficient of the model established by multiple linear regression was the highest.3)The model precision R~2 of 60° canopy image and leaf nitrogen content is 0.8627,RMSE is0.591,RPD is 1.741,the model precision R~2 of 60 ° canopy image and chlorophyll is 0.8904,RMSE is 0.356,RPD is 2.765,the model precision R~2 of 60° canopy image and leaf water content is0.8442,RMSE is 0.027,RPD is 2.094.The results show that it is feasible to use image processing technology combined with the optimal model to analyze the characteristic parameters of winter wheat leaf color to diagnose the growth of winter wheat.4)The accuracy of leaf nitrogen content is about 86%,chlorophyll content is about 89%,and leaf water content is about 84%.The results are reliable and stable.