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Rice plants' ability to develop lodging resistance is essential for their proper growth and development, and understanding the stress–strain relationship is crucial for a comprehensive analysis of this resilience. Nevertheless, significant data variability, inefficiency, and substantial observational inaccuracies hinder current measurement and analysis techniques. Therefore, this study proposes a machine vision-based stress–strain measurement method for rice plants to address these limitations. The technique primarily involves the implementation of the proposed MV-SSRP rotating target detection network, which enhances the model's ability to predict the strain of rice stalks accurately when subjected to bending forces through the integration of the spatial channel reorganization convolution (ScConv) and Squeeze-and-Excitation (SE) attention mechanism. A stress–strain dynamic relationship model was also developed by incorporating real-time stress data obtained from a mechanical testing device. The experimental findings demonstrated that MV-SSRP attained precision, recall, and mean average precision (mAP) rates of 93.4%, 92.6%, and 97.6%, respectively, in the context of target detection. These metrics represented improvements of 4.8%, 3.8%, and 5.1%, respectively, over the performance of the YOLOv8sOBB model. This investigation contributes a theoretical framework and technical underpinning for examining rice lodging resistance. [ABSTRACT FROM AUTHOR]

Oudemansiella raphanipies has gradually gained more and more popularity in the market for its delicious taste, while enhancing human immunity and regulating human body functions as well. To achieve the high-throughput and automatic monitoring of the phenotypes of Oudemansiella raphanipies, a novel method, based on YOLO v4 and Distance Filter (DF), was proposed for high-precision diameter estimation of Oudemansiella raphanipies caps. To begin with, a dataset of Oudemansiella raphanipies was established by the laboratory cultivation and collection of factory samples. The improved YOLO v4 target detection model with added CBAM modules to each convolution block in the backbone was trained to locate the caps and, thus, obtain an approximate bounding box. Secondly, the approximate contour of the cap was gained through the H component, canny edge detection operators, and distance filtering to conduct the noise elimination. Finally, the center of the fitted circle and its accurate contour of the cap could be obtained by the constrained least square method, and the diameter of the fitted circle was estimated by the calibration data. The results of practical tests showed that this method achieved an accuracy of 95.36% in recognizing Oudemansiella raphanipies caps in the growing bed, and the fitting effect of caps was superior to Circle Hough Transform (CHT), the least square method (LS), and Ransac, with no manual adjustment on parameters. Compared with the manual measurement, the mean absolute error (MAE) of this method was 0.77 mm, the coefficient of determination (R2) was 0.95, and the root mean square error (RMSE) was 0.96 mm. Therefore, the model had high-cost performance and could meet the needs of continuous and long-term tracking of the cap shape of Oudemansiella raphanipies, providing the basis for future high-throughput breeding and machine picking. [ABSTRACT FROM AUTHOR]