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Prostrate growth 1 (PROG1) gene is vital in controlling the prostrate growth habit of rice. Studying the effect of PROG1 gene on rice canopy structure is crucial in elucidating the molecular mechanism of rice plant type evolution. Herein, the morphological characteristics of different rice genotypes were collected at different growth stages and leaf nodes using image processing techniques. The morphological characteristics included leaf length, leaf width, and leaf area. The image processing techniques involved boundary mean oscillation (BMO) filtering and minimum bounding rectangle extraction of the target image. On this basis, the effect of the PROG1 gene on rice leaf morphology was quantitatively assessed. Also, the feasibility of image processing techniques in detecting the morphological characteristics of rice leaves was discussed. Under the influence of the PROG1 gene, the length, width, and area of rice leaves decreased by 45.1%, 12.7%, and 44.8%, respectively, at the booting stage. Similarly, the length, width, and area of flag leaves decreased by 15.8%, 32.0%, and 33.7% at the heading stage and by 25.4%, 16.2%, and 19.7% at the filling stage, respectively, and that of secondary leaf reduced by 23.2%, 13.6%, and 54.2% at heading stage and by 24.1%, 17.3%, and 37.0% at filling stage, respectively. Furthermore, the length, width, and area of other leaves reduced by 32.3%, 9.8%, and 51.6% at the heading stage and by 28.6%, 7.3%, and 36.7% at the filling stage, respectively. The leaves in the rice canopy were shorter, narrower, and smaller in leaf area. Notably, no significant differences were found between image processing technology and manual measurement methods regarding the values of leaf morphological characteristics obtained ( P &lt; 0.05 ). Thus, these results show that image processing technology is effective in studying the morphological characteristics of rice leaves. This study provides a reliable foundation for molecular breeding studies and will guide the application of the PROG1 gene in molecular breeding.

Quantitative Evaluation of Leaf Morphology with Different Rice Genotypes Based on Image Processing