The yield of rice is closely related to the panicle number and the panicle weight of rice. The accurate prediction of rice yield can accelerate the speed of breeding. In order to study the relationship between rice yield and rice phenotypic characteristics,the visible light images combined with image processing technology was used for extracting feature of potted rice. 51 phenotypic traits of whole rice were obtained. Combined with deep learning technology,the Faster R convolutional neural network training model was used to detect the number of rice spikes. At the same time,the SegNet model was trained using the SegNet network framework to segment the rice spikes to obtain the binary image of the rice spikes. 33 phenotypic feature data of the panicle were extracted with image processing technology. A total of 85 phenotypic parameters of color,shape,and texture were extracted,and all 85 data were normalized. The 85 phenotypic data normalized were gradually linearly regressed with the fresh and dry quality of rice panicle,and the correlation was selected. The artificial measurement data in the experiment included the fresh weight and dry weight of potted rice panicle. The models of predicting fresh and dry panicle weight of potted rice were established separately by using panicle number and characteristic panicles,51 characteristics of whole plants and all 85 characteristics of high correlation characteristic data. The prediction model was optimized according to the determination coefficientR2,mean relative error (MAPE) and standard deviation of absolute relative error (SAPE). The optimal prediction model was selected according to the decision coefficient R2,average relative error (MAPE) and standard deviation of relative absolute value (SAPE) of the model. The results of prediction showed that the effect of predicting panicle characteristics is the best. The decision coefficients R2 of the predicted value and the real value of the model with the best effect are 0.787±0.051 and 0.840±0.054,respectively. Combined with deep learning,the number and characteristics of panicle difficult to obtain automatically by traditional methods are extracted. It will provide a new idea and method for predicting rice panicle weight,and further improving the accuracy of predicting rice panicle weight.