To determine the patterns and influencing factors of the mechanical properties of millet stems from different varieties during the maturity period, this study employed a complete block experiment method and conducted shearing and bending tests on millet stems using the INSTRON5544 electronic universal material testing machine. The research investigated the variation in the shear strength, specific shear energy, bending strength, elastic modulus, and bending stiffness at different internode positions of the stems of Changza 466, Zhangza 16, and Jingu 21 during their maturity period. The results indicated that the variety had a significant impact on the mechanical properties of millet stems: from largest to smallest, the order of shear and bending forces was Jingu 21, Zhangza 16, and Changza 466. The shear strength and bending strength of Jingu 21 were the greatest among the three stem samples. The internode position significantly affected the shear mechanical properties of the millet stems, showing a general trend of decreasing shear strength with ascending internode position. The effect of the internode position on the bending stiffness was highly significant, whereas its impact on the bending strength and elastic modulus was not significant. The shear strength of the millet stems ranged from 3.866 ± 1.086 to 6.953 ± 2.208 MPa, significantly lower than the bending strength, which ranged from 18.934 ± 4.374 to 34.286 ± 6.875 MPa. The lowest shear strength and specific shearing energy, recorded at the fifth internode, were 4.028 ± 1.918 MPa and 15.097 ± 12.633 MJ/mm², respectively. Jingu 21 and Changza 466 exhibit better lodging resistance than Zhangza 16. It is recommended to use a cutting-type platform for harvesting millet stems, with the cutting height set at the fifth internode position. This study provides a theoretical basis for the design of millet harvesting machinery and the selection of harvest parameters.

Mechanized harvesting is a crucial step in the agricultural production of foxtail millet (Setaria italica), as its peduncles are susceptible to bending and breaking during the harvesting process, leading to yield losses and deterioration in grain quality. To evaluate the suitability of foxtail millet for mechanical harvesting, this study comprehensively analyzed the biomechanical properties of the peduncles and related biological morphological characteristics of 116 foxtail millet accessions, establishing a system for indicator screening and comprehensive evaluation. Using partial correlation analysis and R-type cluster analysis, four biomechanical and seven related morphological indices of the peduncle were screened from 22 candidate indicators, with their coefficient of variation ranging from 6% to 80%. The entropy method was used to assign weights to the selected indices, with biomechanical factors contributing 47.4%, peduncle morphology 20.2%, spike morphology 27.6%, and plant height 4.8%. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Rank-Sum Ratio (RSR) methods were applied to rank and grade the classification of the 116 foxtail millet varieties into four performance groups: Excellent (8 varieties), Good (50 varieties), Moderate (51 varieties), and Poor (7 varieties). This study provides a scientific basis for the selection and evaluation of foxtail millet varieties.

Accurate leaf area measurement is essential for plant growth monitoring and ecological research; however, it is often challenged by perspective distortion and color inconsistencies resulting from variations in shooting conditions and plant status. To address these issues, this study proposes a visual and semi-automatic measurement system. The system utilizes Hough transform-based perspective transformation to correct perspective distortions and incorporates manually sampled points to obtain prior color information, effectively mitigating color inconsistency. Based on this prior knowledge, the level-set function is automatically initialized. The leaf extraction is achieved through level-set curve evolution that minimizes an energy function derived from a multivariate Gaussian distribution model, and the evolution process allows visual monitoring of the leaf extraction progress. Experimental results demonstrate robust performance under diverse conditions: the standard deviation remains below 1 cm², the relative error is under 1%, the coefficient of variation is less than 3%, and processing time is under 10 s for most images. Compared to the traditional labor-intensive and time-consuming manual photocopy-weighing approach, as well as OpenPheno (which lacks parameter adjustability) and ImageJ 1.54g (whose results are highly operator-dependent), the proposed system provides a more flexible, controllable, and robust semi-automatic solution. It significantly reduces operational barriers while enhancing measurement stability, demonstrating considerable practical application value.