Research On Camellia Flower Identification Technique Based On Modified YOLOv3

At present,the area of camellia 466.7 hectares,camellia output of 2.5 million tons.Artificial pollination is an effective measure to improve the fruit setting rate of camellia oil,so it is necessary to pick camellia oil flowers for powder.Accurate identification of camellia camellia is one of the key technologies to realize its intelligent picking.This paper proposes the image recognition technology of camellia camellia camellia camellia based on deep learning.The main research contents are as follows:1.Establishment of camellia oleifera image database.Acquisition and pretreatment;analyze the basic characteristics of camellia oleifera in natural state and establish a data set,which can completely represent the illumination,outline and diversity of stacked blocking posture.2.Based on the recognition method and realization of camellia oleifera by image segmentation,OTSU image segmentation algorithm is used to segment camellia oleifera,obtain the target area of camellia oleifera,extract row feature information,and finally use SVM to classify the extracted feature information.The recognition rate of flowering status was 0.68 and 0.73 for P.oleifera flowers.3.Based on the neural network of camellia flower target detection method and implementation,compare the current commonly used depth based on the convolutional neural network of target detection model,analyze the advantages and disadvantages of various methods,establish the detection model of evaluation index and parameter adjustment,design group test,select the optimal parameter combination for network training,and the test to verify the effectiveness and feasibility of the algorithm,select the optimal network model.4.Put forward the improvement method of YOLOv3 network model,which includes the study of increasing attention mechanism of the network model,introducing the residual structure module and improving the k-means clustering algorithm.Finally,the flowering degree and growth status of camellia camellia based on the improved YOLOv3 network model.5.In the camellia flower identification test,the results show that:this paper improved the YOLOv3 network model of the highest average accuracy,the average accuracy of three stages of growth of 92.89%,93.29%and 96.26%respectively,mAP value is 94.15%,the average detection time is 552ms,has good recognition accuracy and recognition effect,suitable for the identification of camellia tree flower detection under natural environment.The identification and detection method of camellia oleifera proposed in this paper can effectively identify and detect camellia oleifera in the forest,and provide a technical basis for the subsequent realization of intelligent picking of camellia oleifera.