# Evaluation Model of Rice Seeding Production Line Seeding Quality Based on Deep Learning
## 1 Introduction and Related Work
### Related Work  
To date, a multitude of studies have been dedicated to evaluating the quality of rice seeding, incorporating diverse methodologies to identify instances of seed omission during the sowing process. These methods vary in approach and accuracy, offering a range of strategies to ensure optimal seed distribution and maximize agricultural efficiency. Among these, [PERSON] et al. (2018) have proposed a method for assessing the quality of seed sowing in rice seedling trays based on an improved shape factor. This method employs threshold segmentation, morphological algorithms, and mask positioning techniques to extract the contours of seeds within each cell of the seedling tray. By employing connected component detection, it calculates the number of seeds in each cell, providing valuable data for subsequent supplementary sowing. [PERSON] et al. (2018) developed a fixed threshold segmentation algorithm to differentiate grid images from seed images within seedling tray photographs. They also devised a technique to extract grid line pixel coordinates from grid images, which entails scanning contours within each image to inspect seed presence in cells, thereby calculating the number of misses and ultimately determining the missing rate of hybrid rice seeds. The evaluation of seed quality is equally crucial for crops beyond rice. Researchers worldwide have also endeavored to employ deep learning techniques to detect instances of missed seeding in trays for various crops, achieving notable progress. [PERSON] et al. (2018) have developed a sweetcom seed detection method based on a voting mechanism, designed to identify missing seeds in moving seedling trays. This method has demonstrated favorable results in experimental settings. [PERSON] et al. (2018) have innovated an automated supplementary seeding apparatus employing machine vision, adept at identifying unseeded spots in trays even at high velocities. This apparatus, when integrated into a tray-type keel seeding machine, accomplishes supplementary seeding, reducing the rate of unseeded tray holes from an initial 5.37% down to 0.89%. Concurrently, [PERSON] et al. (2018) proposed a perception method for tomato trays, leveraging the YOLOv5x model to detect missing seeds and fill vacant cells. They developed a detection and supplementary seeding mechanism for tomato plug trays and trialed it within a greenhouse. The findings indicated a mean detection precision of 92.84% and a successful re-plantation efficacy of 91.7% on the trays. [PERSON] et al. (2018) proposed a machine vision-based method for detecting missed seeding, utilizing small-diameter and irregularly shaped cabbage, tomato, and radish seeds as experimental subjects. The accuracy of this method in monitoring seed misplacement for vegetable seeds reached 92.5%. Subsequent to analyzing the literature above, it is discernible that the application of machine vision for missed seeding detection is comparatively advanced in diverse agricultural productions. Nonetheless, the studies mentioned above are subject to certain limitations. Firstly, most of the relevant research relies on traditional machine vision techniques such as threshold segmentation, morphological algorithms, and SIFT for seed misplacement detection. These methods often lack the accuracy, generalization ability, and capacity to handle complex scenarios characteristic of deep learning approaches. Additionally, the prevalent focus is on grid-patterned seeding trays, which offer relative ease in positioning and detection. Contrastingly, most contemporary rice seeding trays do not conform to a grid pattern and present a more stochastic seed distribution, increasing the complexity of detection, with scant research addressing seeding quality detection for this tray type. Detecting unseeded areas in rice seeding trays can empower farmers and producers to monitor production, analyze pertinent data, and render decisive support for reseeding tasks (2018).