An automated method for the assessment of the rice grain germination rate

Abstract

The germination rate of rice grain is recognized as one of the most significant indicators of seed quality assessment. Currently, grain germination rate is generally determined manually by experienced researchers, which is time-consuming and labor-intensive. In this paper, a new method is proposed for counting the number of grains and germinated grains. In the coarse segmentation process, the k-means clustering algorithm is applied to obtain rough grain-connected regions. We further refine the segmentation results obtained by the k-means algorithm using a one-dimensional Gaussian filter and a fifth-degree polynomial. Next, the optimal single grain area is determined based on the area distribution curve. Accordingly, the number of grains contained in the connected region is equal to the area of the connected region divided by the optimal single grain area. Finally, a novel algorithm is proposed for counting germinated grains. This algorithm is based on the idea that the length of the intersection between the germ and the grain is less than the circumference of the germ. The experimental results show that the mean absolute error of the proposed method for germination rate is 2.7%. And the performance of the proposed method is robust to changes in grain number, grain varieties, scale, illumination, and rotation.

Exploring eating behaviors, knowledge and attitudes of adolescent Indian girls

“…Understanding the intricate interplay of knowledge, attitudes, and practices [KAP] among adolescents is crucial for devising effective interventions to address the escalating risks associated with NCDs [8].The existing literature underscores the significance of KAP elements in driving behavioural change, with an emphasis on their role in shaping personal eating habits. While foundational nutritional knowledge is typically imparted through school curricula, there remains a dearth of comprehensive exploration into various facets of food knowledge among adolescent [9]. Adolescent dietary choices are influenced by a multitude of factors, ranging from taste preferences and cost considerations to broader socio-cultural norms.…”

RSG-YOLO: Detection of rice seed germination rate based on enhanced YOLOv8 and multi-scale attention feature fusion

“…Predominantly, these studies underscore the importance of exploiting differences in color or length between seeds and embryos to determine germination status. Nonetheless, the similar texture characteristics and size disparities of germinated seeds and non-germinated seeds, can obscure these differences, leading to potential misclassification and reduced accuracy in detection [4].…”

“…Zhang et al unveiled a groundbreaking germination grain counting algorithm based on the premise that the intersection length between the embryo and the grain is shorter than the embryo’s circumference. They improved upon the coarse segmentation results from the k-means clustering algorithm through the application of a one-dimensional Gaussian filter and a fifth-degree polynomial for refinement[4]. Tan et al introduced an algorithm adept at identifying and counting conjoined rice grains.…”