# Rapid Detection of Key Phenotypic Parameters in Wheat Grains Using Linear Array Camera
## 1 Introduction  
As a staple food crop, the yield and quality of wheat are critically linked to global food security and human nutritional health [1, 2, 3]. With ongoing population growth and the increasing diversity of dietary demands, the global need for wheat has been rising. However, challenges such as limited cultivated land area and yield reduction caused bysub-optimal crop grains have increased the necessity to cultivate improved wheat varieties and enhance yield per unit area. Consequently, improving wheat productivity has become a primary focus for breeders [4; 5; 6]. The main determinants of wheat yield include grain weight, the number of fillers, and the ear formation rate. This study analyzes the phenotypic indices of wheat grains, recognizing that traits such as grain length and width are strongly positively correlated with grain weight. By combining grain phenotypic measurements with bud point determinations, this study offers a comprehensive framework for evaluating germplasm resources [7]. The continuous improvement of agricultural information has promoted the extensive application of technologies, such as machine vision, infrared spectroscopy, and image processing, in the agricultural field [8]. These technologies have made substantial progress in primary areas, including disease diagnosis [9], variety classification [10; 11; 12; 13], grain detection [14; 15], and yield prediction [16; 17]. For instance, [PERSON] et al. [18] used a visible-near-infrared hyperspectral imaging system to detect sprouting damage in red spring wheat, demonstrating that reflectance at 878 nm and 728 nm effectively differentiates unsprouted, sprouted, and severely sprouted wheat across the 400-1000 nm spectral range. Similarly, [PERSON] et al. [19] used a hyperspectral imaging system to obtain spectral information from wheat grain samples for seed viability discrimination. By applying multiple pretreatment techniques, characteristic band extraction methods, and coupled analyses, they developed a MUCS-PLS-DA model that achieved a germination rate prediction accuracy of 93.1%. These findings enhance the extensive research exploiting near-infrared hyperspectral data in wheat grain analysis. However, hyperspectral data contain a large amount of redundant information and require feature extraction. Moreover, the data analysis and processing steps are complex, and most breeding researchers cannot analyze spectral data. Furthermore, real-time analysis remains difficult with hyperspectral technology due to its computational intensity and data volume, highlighting the need for simplified, rapid phenotyping methods to better support breeding programs. As the foundation of machine vision technology, image processing technology enables quick and precise analyses of various grain characteristics through operations such as the digital processing of images and feature extraction [8; 20]. For example, it can be applied to assess attributes such as the color and morphology of wheat grains, as well as structural features, including awns, spike shapes, and the number of wheat ears [21; 22]. In traditional methods, detecting the appearance and quality of wheat grains mainly relies on manual inspection [23]. Researchers conduct relevant work based on their own experience, which is inefficient and subject to strong subjective factors, making it difficult to meet detection needs in scientific research scenarios. Consequently, obtaining quick and accurate wheat grain phenotypic information through image processing is of great significance. For example, [PERSON] [24] improved the existing image processing methods and proposed an improved marker watershed algorithm for grain segmentation. [PERSON] [25] applied preprocessing techniques to raw wheat grain images that preserved grain shape, thereby facilitating the reliable extraction of color, shape, and texture features, which are essential for further analysis. Similarly, [PERSON] et al.