Background: The number grain per panicle of rice is an important phenotypic trait and a significant index for variety screening and cultivation management. The methods that are currently used to count the number of grains per panicle are manually conducted, making them labor intensive and time consuming. Existing image-based grain counting methods had difficulty in separating overlapped grains. Results: In this study, we aimed to develop an image analysis-based method to quickly quantify the number of rice grains per panicle. We compared the counting accuracy of several methods among different image acquisition devices and multiple panicle shapes on both Indica and Japonica subspecies of rice. The linear regression model developed in this study had a grain counting accuracy greater than 96% and 97% for Japonica and Indica rice, respectively. Moreover, while the deep learning model that we used was more time consuming than the linear regression model, the average counting accuracy was greater than 99%. Conclusions: We developed a rice grain counting method that accurately counts the number of grains on a detached panicle, and believe this method can be a huge asset for guiding the development of high throughput methods for counting the grain number per panicle in other crops.
Southern China is the birthplace of rice-cultivating agriculture and different language families and has also witnessed various human migrations that facilitated cultural diffusions. The fine-scale demographic history in situ that forms present-day local populations, however, remains unclear. To comprehensively cover the genetic diversity in East and Southeast Asia, we generated genome-wide SNP data from 211 present-day Southern Chinese and co-analyzed them with ∼1,200 ancient and modern genomes. In Southern China, language classification is significantly associated with genetic variation but with a different extent of predictability, and there is strong evidence for recent shared genetic history particularly in Hmong–Mien and Austronesian speakers. A geography-related genetic sub-structure that represents the major genetic variation in Southern East Asians is established pre-Holocene and its extremes are represented by Neolithic Fujianese and First Farmers in Mainland Southeast Asia. This sub-structure is largely reduced by admixture in ancient Southern Chinese since > ∼2,000 BP, which forms a “Southern Chinese Cluster” with a high level of genetic homogeneity. Further admixture characterizes the demographic history of the majority of Hmong–Mien speakers and some Kra-Dai speakers in Southwest China happened ∼1,500–1,000 BP, coeval to the reigns of local chiefdoms. In Yellow River Basin, we identify a connection of local populations to genetic sub-structure in Southern China with geographical correspondence appearing > ∼9,000 BP, while the gene flow likely closely related to “Southern Chinese Cluster” since the Longshan period (∼5,000–4,000 BP) forms ancestry profile of Han Chinese Cline.
BackgroundThe number of cultivated wheat seedlings per unit area allows calculation of plant density. Wheat seedling density provides emergence data and this is useful for improving crop management. The number of wheat seedlings is typically determined by visual counts but this is time-consuming and laborious.ResultsWe obtained field digital images of 1st to 3rd leaf stage wheat seedlings. The seedlings were extracted using an image analysis technique that calculated the coverage degree of the seedlings and the number of angular points of overlapping leaves. The wheat seedling quantity estimation model was constructed using multivariate regression analysis. The model parameters included coverage degree, number of angular points, variety coefficient, and leaf age. Introduction of the number of angular points increased the accuracy of the single coverage degree model. The R2 value was consistently > 0.95 when the model was applied to different varieties, indicating that the model was adaptable for different varieties. As the leaf stage or density increased, the accuracy of the model declined, but the minimum R2 remained > 0.87, indicating good adaptability of the model to seedlings with different leaf ages and densities.ConclusionsThis method is an effective means for counting wheat seedlings in the 1st to the 3rd leaf stages.
Head rice rate is an important factor affecting rice quality. In this study, an inflection point detection-based technology was applied to measure the head rice rate by combining a vibrator and a conveyor belt for bulk grain image acquisition. The edge center mode proportion method (ECMP) was applied for concave points matching in which concave matching and separation was performed with collaborative constraint conditions followed by rice length calculation with a minimum enclosing rectangle (MER) to identify the head rice. Finally, the head rice rate was calculated using the sum area of head rice to the overall coverage of rice. Results showed that bulk grain image acquisition can be realized with test equipment, and the accuracy rate of separation of both indica rice and japonica rice exceeded 95%. An increase in the number of rice did not significantly affect ECMP and MER. High accuracy can be ensured with MER to calculate head rice rate by narrowing down its relative error between real values less than 3%. The test results show that the method is reliable as a reference for head rice rate calculation studies.
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