Regenerative ability is closely related to the number of regenerated buds in the ratooning season, even the yield of ratooning rice. The traditional detection cannot fully meet the large-scale production in the rice regenerated buds, due to contact damage, subjective inefficiency, and low repeatability. In this study, a multi-target tracking of regenerated buds was proposed for high-precision counting using Micro-CT (computed tomography) and an improved DeepSORT. Micro-CT imaging was first adopted to capture the cross-section video stream of rice stem in the ratooning season. Then, the YOLOv5s network was used as the tracking detector of regenerated buds, while the improved DeepSORT tracking was studied to achieve the accurate tracking and counting of rice regenerated buds. The ID discrepancy was then optimized to improve DeepSORT tracking. The matching accuracy of the multi-target tracking increased using the feature of continuity between each frame of CT tomogram images, indicating the substantially improved ID switch. Finally, the height of the regenerated buds was calculated to discriminate the effectively regenerated buds using the location information of the tracking object. The experimental results showed that the Mean Average Precision (mAP) values of YOLOv5s were 97.3% and 99.1% for the regenerated buds and stalks, respectively, in the target detection. The multi-object tracking accuracy (F_MOTA ) , higher order tracking accuracy(F_HOTA) , and ID switch of the improved DeepSORT were 77.61%, 61.73%, and 6, respectively, in the multi-target tracking, compared with the original. Furthermore, the F_MOTA and F_HOTA were improved by 1.51% and 8.5%, respectively, whereas the ID switch was improved by 94%. The multi-object tracking efficiency of the DeepSORT and the improved were 25, and 24 frames per second, respectively, without a significant decrease in the efficiency. The system and manual measurements of 104 pots of ratooning rice were used to verify the regenerated buds, where the correlation coefficient square R² of 0.983, the root mean square error of 3.460, and the average absolute percentage error of 5.647%, indicating a better consistency with the manual measurement. The ratio of the regenerated buds to the number of the stem was computed for the early ratooning ability of rice. The correlation analysis was also performed between the ratooning ability of two rice varieties in 38 pots and the actual yield in the ratooning season. It was found the R² values were 0.795 and 0.764, respectively, indicating a significant positive correlation between the regenerative ability and rice yield. In conclusion, the novel nondestructive way was achieved to detect the regenerated buds in the early regenerative ability measurement. The finding can also provide important technical support for the ratooning rice breeding.

The panicle number and length are two of the most crucial indicators of rice yield. Accurate acquisition of the panicle traits is of great significance to rice breeding and genetic research. However, the traditional measurements of panicle traits cannot fully meet the large-scale production in recent years, due to the contact damage, subjective inefficiency, and low repeatability. Therefore, it is very urgent to develop the observation and identification for the accurate and efficient measurement of rice panicle traits. In this study, the rice panicle tracking and traits extraction were proposed using optical flow preprocessing and the StrongSORT algorithm. Initially, a series of experiments was conducted to capture the 200 rotating rice videos. The dataset was divided into the training and testing sets in a ratio of 8:2. Subsequently, the Gunnar Farneback optical flow algorithm was employed to preprocess the videos in order to reduce the occlusion. The Convolutional Block Attention Module (CBAM) attention mechanism was then integrated into the YOLOv8-seg network in order to enhance the target detection and segmentation of rice panicles. Finally, the StrongSORT algorithm was utilized to realize the multi-target tracking and the counting of rice panicles. The Zhang-Suen skeleton extraction was applied to determine the length of the rice panicle with the largest panicle after detection. Moreover, a motion prior model was constructed with the movement trajectories and velocities of the potted rice. The position of rice panicles was predicted in the next frame. The ID switches were reduced to prevent the panicle tracking failures and duplicate counting caused by occlusion. The results demonstrated that high accuracy of the tracking was achieved to detect the rice panicle. The mean average precision of the improved YOLOv8-seg model reached 81.1%, with an increase of 8.7 percentage points, compared with the original YOLOv8-seg model. Furthermore, the mAP of the YOLOv8-seg model was improved to 95.0% after optical flow preprocessing, indicating a substantial enhancement of 13.9 percentage points over the unprocessed model. In rice multi-target tracking, the combination of optical flow preprocessing, the improved YOLOv8-seg, and StrongSORT was achieved in a multi-target tracking accuracy of 85.58% and a high-order tracking accuracy of 64.06%, which were improved by 11.83 and 9.53 percentage points, respectively, compared with the combination without optical flow preprocessing. The number of ID switches was significantly reduced from 891 to 275, with a decrease of 69.2%. In terms of counting accuracy, the combination was achieved in a coefficient of determination (R²) of 0.969 6, a mean absolute percentage error of 2.15%, and a root-mean-square error of 1.87, compared with the actual values. In panicle length extraction, the R² value was 0.940 8, the MAPE was 4.07%, and the RMSE was 0.47. The combination of the optical flow preprocessing, improved YOLOv8-seg, and StrongSORT effectively reduced the interference among overlapping panicles and the ID switch. The accuracy and multi-target tracking were enhanced after detection at the same time. The finding can also provide the technical pathway for the rice panicles and length measurement in rice breeding.

Cotton is one of the most important economic crops in the world, and verticillium wilt is the number one disease in major cotton production areas in the world. Verticillium wilt causes the leaves to wilt, fade and even fall off by infecting the roots of cotton, resulting in severe decline both cotton quality and its yield. The national standard divides the leaves suffering from verticillium wilt into five grades. The traditional detection method mainly relies on manual labor, which has problems such as subjectivity, inefficiency, and poor repeatability. The cotton verticillium wilt disease classification method, which is based on VFNet-Improved, Deep Sort, and collision line matching mechanisms as the main algorithm framework, realizes the statistics of the number of diseased leaves and the classification of the disease level under the condition of rotating video input. Based on the VFNet target detection network, the research first combined multi-scale training, dynamic convolution and other optimization methods to achieve accurate positioning of diseased leaves in rotating videos. Then the Deep Sort tracker was used to realize the correlation of the same leaf in the front and back frames, and a mask collision line matching mechanism was designed for the ID jump problem in the tracking process; finally, OpenCV was used to perform feature extraction and disease classification of leaves passing the mask line. The results showed that the VFNet-Improved model could achieve the best effect in the object detection algorithms with 0.906 mAP₇₅, which was 0.012 higher than the VFNet model, and the FPS reached 12.9 frames/s. The tracking result MOTA of the Deep Sort was 0.835. R², RMSE, MAE and MAPE of VFNet-Improved were 0.890, 5.138, 4.300 and 14.967% respectively, which performed high consistency with manual measurements. In conclusion, this study has demonstrated a novel tool for the accurate and efficient evaluation of cotton verticillium wilt, which would be of great significance for the cotton breeding and genetic analysis research.