Detection Method of Effective Tillering of Rice in Field Based on Lightweight Ghost-YOLOv8 and Smart Phone

Abstract

[Objective]The number of effective tillers per plant is one of the important agronomic traits affecting rice yield. In order to solve the problems of high cost and low accuracy of effective tiller detection caused by dense tillers, mutual occlusion and ineffective tillers in rice, a method for dividing effective tillers and ineffective tillers in rice was proposed. Combined with the deep learning model, a high-throughput and low-cost mobile phone App for effective tiller detection in rice was developed to solve the practical problems of effective tiller investigation in rice under field conditions.[Methods]The investigations of rice tillering showed that the number of effective tillers of rice was often higher than that of ineffective tillers. Based on the difference in growth height between effective and ineffective tillers of rice, a new method for distinguishing effective tillers from ineffective tillers was proposed. A fixed height position of rice plants was selected to divide effective tillers from ineffective tillers, and rice was harvested at this position. After harvesting, cross-sectional images of rice tillering stems were taken using a mobile phone, and the stems were detected and counted by the YOLOv8 model. Only the cross-section of the stem was identified during detection, while the cross-section of the panicle was not identified. The number of effective tillers of rice was determined by the number of detected stems. In order to meet the needs of field work, a mobile phone App for effective tiller detection of rice was developed for real-time detection. GhostNet was used to lighten the YOLOv8 model. Ghost Bottle-Neck was integrated into C2f to replace the original BottleNeck to form C2f-Ghost module, and then the ordinary convolution in the network was replaced by Ghost convolution to reduce the complexity of the model. Based on the lightweight Ghost-YOLOv8 model, a mobile App for effective tiller detection of rice was designed and constructed using the Android Studio development platform and intranet penetration counting.[Results and Discussions]The results of field experiments showed that there were differences in the growth height of effective tillers and ineffective tillers of rice. The range of 52 % to 55 % of the total plant height of rice plants was selected for harvesting, and the number of stems was counted as the number of effective tillers per plant. The range was used as the division standard of effective tillers and ineffective tillers of rice. The accuracy and recall rate of effective tillers counting exceeded 99%, indicating that the standard was accurate and comprehensive in guiding effective tillers counting. Using the GhostNet lightweight YOLOv8 model, the parameter quantity of the lightweight Ghost-YOLOv8 model was reduced by 43%, the FPS was increased by 3.9, the accuracy rate was 0.988, the recall rate was 0.980, and the mAP was 0.994. The model still maintains excellent performance while light weighting. Based on the lightweight Ghost-YOLOv8 model, a mobile phone App for detecting effective tillers of rice was developed. The App was tested on 100 cross-sectional images of rice stems collected under the classification criteria established in this study. Compared with the results of manual counting of effective tillers per plant, the accuracy of the App's prediction results was 99.61%, the recall rate was 98.76%, and the coefficient of determination was 0.985 9, indicating the reliability of the App and the established standards in detecting effective tillers of rice.[Conclusions]Through the lightweight Ghost-YOLOv8 model, the number of stems in the cross-sectional images of stems collected under the standard was detected to obtain the effective tiller number of rice. An Android-side rice effective tillering detection App was developed, which can meet the field investigation of rice effective tillering, help breeders to collect data efficiently, and provide a basis for field prediction of rice yield. Further research could supplement the cross-sectional image dataset of multiple rice stems to enable simultaneous measurement of effective tillers across multiple rice plants and improve work efficiency. Further optimization and enhancement of the App's functionality is necessary to provide more tiller-related traits, such as tiller angle.

Keywords

• effective tillers of rice
• android
• yolov8
• ghostnet
• app
• phenotype