Study On The Diagnosis Model Of Tea Plant Nitrogen Nutrition Level Based On Superpixel Segmentation

Nitrogen?N?is an essential nutrient element for crop growth.According to the nitrogen nutrition level of crops,rational and precise application of nitrogen fertilizer can not only improve the yield and quality of crops,but also protect the environment,save resources,alleviate the soil acidification,soil degradation,water pollution and other problems caused by excessive fertilization,which is also the practical requirement of digital and green agriculture in China.Since traditional crop nitrogen nutrition diagnosis methods are complex,time-consuming and labor-intensive,the use of digital images for rapid non-destructive diagnosis of crop nitrogen nutrition has become one of the research hotspots in recent years.Took the"Nongkangzao"tea plant in the experimental field as the research object,based on the image processing technology,the nitrogen nutrition diagnosis model of tea plant in the natural environment was explored,and the research results were applied to the monitoring platform.The main contributions are as follows:?1?A system for automatic collection of tea plant images based on a monitoring platform.To obtain digital images of the tea plant more conveniently,a monitoring system was installed in the tea plantation.According to the characteristics of the PTZ?pan/tilt/zoom?camera,a timer automatic cruise snapshot algorithm was designed.While collecting data for research,a tea plant images database was established to provide an intuitive and traceable data source for tea plant nitrogen nutrition diagnosis.?2?Tea plant image segmentation algorithm based on improved simple linear iterative clustering?SLIC?and support vector machine?SVM?classification.Aiming at the defect that the current image processing technology is easily affected by the sunlight,after analyzing the shortcomings of the traditional image segmentation algorithms,the colorimetrical characteristics of agricultural images was further analyzed,and then the superpixel algorithm SLIC was improved.The improved superpixel segmentation algorithm combined with SVM classification was used to segment the reflective area and background area of the tea plant image.By comparing with SLIC and the other two improved algorithms,the effectiveness of the improved algorithm in this paper was verified.In the case of changing sunlight,the characteristic parameters of the tea plant image segmented by the improved SLIC+SVM are more stable than those of the undivided image,which lays a foundation for the application of image processing technology in the natural environment.?3?Diagnosis model of tea nitrogen nutrition.The correlation between the measured nitrogen content of the tea plant and 19 color characteristic parameters of tea canopy image was analyzed.It was found that the nitrogen content of the tea plant was significantly correlated with most color characteristic parameters.Then,three algorithms,stepwise multiple linear regression?SMLR?,back propagation neural network?BPNN?and support vector machine regression?SVR?,were used to establish the diagnosis models of nitrogen content of tea plant.The generalization ability of the model was verified by the test data set and error analysis was carried out.Then the model was optimized from different aspects.After evaluating the error indexs of the optimized model,the neural network segmentation model based on the limited-memory BFGS?L-BFGS?algorithm was finally determined as the optimal diagnosis model of tea nitrogen content.The root mean square error?RMSE?and mean relative error?MRE?of the model on the test data set of subset N₁?N?2.97%?are 0.112,3.97%,and the RMSE and MRE on the test data set of subset N₂?N>2.97%?are 0.118,2.83%,which has certain practical application value.