# Rapid Detection of Key Phenotypic Parameters in Wheat Grains Using Linear Array Camera
## 4 Discussion  
In this study, traditional image processing techniques, including morphological operations and geometric analysis, were employed instead of more recent deep learning approaches. Although deep learning methods have demonstrated good performance in image feature extraction [35], they often necessitate extensive labeled data during the initial phases of dataset construction and require substantial computational resources. Conversely, traditional image processing methods [36; 37; 38] exhibit increased strength and reliability when working with small sample sizes and limited datasets. Moreover, images acquired using linear array cameras possess higher spatial resolution and generate larger data volumes than images captured by conventional area-array cameras or mobile devices, thereby containing rich phenotypic information. While this enhances the detail and quality of the data, it also significantly increases the complexity and time required for dataset construction and preprocessing. Furthermore, the training characteristics and development of deep learning models in agricultural applications often present interpretability challenges, particularly when applied across different crop grains with variable morphology [39]. However, the traditional image processing methods are computationally less intensive, easier to tune, and more transparent in operation when dealing with moderately complex image features. This makes them particularly advantageous in agricultural vision systems where the target objects have relatively stable and uniform backgrounds. For example, [PERSON] [24], [PERSON] [27], [PERSON] [28], and [PERSON] [40] have demonstrated the effectiveness of traditional image processing techniques for the rapid and accurate extraction of grain parameters. Considering factors such as data accessibility, model interpretability, and computational resource constraints, traditional image processing methods offer a more practical and suitable approach for the objectives and experimental conditions of this study. We used linear array cameras combined with image processing technology, successfully achieving the rapid and accurate detection of the key phenotypic parameters of wheat grains. The proposed image processing method demonstrates excellent agreement with manual measurements, yielding a determination coefficient of 0.986 for grain length and 0.9505 for grain width. Furthermore, the root mean square error values for these measurements are below 0.1, indicating a high level of precision in quantifying the geometric dimensions of wheat grains. Previous studies have demonstrated the strength and applicability of traditional image processing techniques among various crops. For instance, [PERSON] [24] enhanced wheat feature extraction through adaptive thresholding, [PERSON] [26] used similar techniques for maize kernel detection, [PERSON] et al. [41] achieved a classification accuracy of over 0.90 for king oyster mushrooms, and [PERSON] et al. [27]effectively extracted wheat ear features with an appearance recognition accuracy of over 0.88. These studies collectively validate the reliability and effectiveness of image processing techniques in agricultural phenotyping. Moreover, when compared with deep learning-based phenotypic extraction methods--such as the ImCascade R-CNN model developed by [PERSON] et al. [42]--it demonstrated excellent performance. The coefficient of determination for grain length and width improved by 0.051 and 0.129, respectively, showing that well-designed image processing methods can work better than complex deep learning models, especially when there are limited data computing resources. This confirms the feasibility of combining high-precision linear array cameras with image processing technology to accurately obtain crop phenotypic parameters.