A Rice Transcription Factor Controls Grain Length through Cell Number

Cox, Naomi; Smith, Lisa M.

doi:10.1104/pp.19.00730

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Another important yield component in rice is the 1,000-grain weight. However, it is more specific to the genotype than a response to fertilization (Cox & Smith, 2019). As demonstrated in Table 2, this study also found that the applied Si on 1,000-grain weight was nonsignificant over the control.…”

Section: Effect Of Silicon On Yield Attributes Of Ricesupporting

confidence: 54%

Optimizing Silicon Application to Improve Growth, Grain Yield, and Nutrient Uptake of indica Rice (Oryza sativa cv. Bw 367)

Rupasinghe¹,

Hanafi²,

Yusop³

et al. 2022

JTAS

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The rice plant accumulates silicon (Si) in greater quantity, which varies among the rice genotypes. This study was conducted to determine the optimum fertilization rate and its effect on growth, yield, yield attributes, and soil nutrient uptake. Six different silicon dioxide (SiO2) rates, including 0, 50, 75, 100, 125, and 150 kg SiO2/ha, were applied initially. The optimum rate of SiO2 was obtained by statistical analysis, utilizing the analysis of variance (ANOVA) and Duncan’s Multiple Range Test (DMRT) to separate the means. The results showed that shoot dry weight and plant height were significantly affected by Si fertilization. The highest Si tissue concentration of 395.27 μg/100 mg was recorded in 100 kg SiO2/ha treated plants, and their potassium, phosphorous, silicon, and magnesium uptakes were increased by 2, 1.3, 11 and 1.8 folds, respectively. Further, in yield attributes, 32 and 52% increments and a 30% decrease were observed in the total number of grains, filled grains, and unfilled grains per panicle, respectively, and were not significantly different from those observed in 125 kg SiO2/ha rate. The highest grain yield of 104.6 g/pot was obtained with 100 kg SiO2/ha level of Si fertilizer, and it was statistically at par with the yields obtained with 125 kg SiO2/ha. The quadratic function found the rate of Si fertilizer for optimum grain yield (100.5 g/pot) as 115 kg SiO2/ha; thus, it could be concluded that indica rice genotypes need to be fertilized with 115 kg SiO2/ha for optimum yield for higher growth and nutrient uptake.

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Section: Effect Of Silicon On Yield Attributes Of Ricesupporting

confidence: 54%

Optimizing Silicon Application to Improve Growth, Grain Yield, and Nutrient Uptake of indica Rice (Oryza sativa cv. Bw 367)

Rupasinghe¹,

Hanafi²,

Yusop³

et al. 2022

JTAS

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“…The increase of panicle length affected the yield. This condition, due to each panicle, affected the branches of grain stalk that produced more grain [20]. The length of a panicle of rice in SRI method was longer than the conventional method [8].…”

Section: A Length Of Paniclementioning

confidence: 95%

Land Flooding Effect Before and After Planting on Rice Yield in the System of Rice Intensification

Dwipa¹,

Kasim²,

Rozen³

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Rice is the primary carbohydrate source for half world population, particularly for Asian people, and rice demand also increases for non-Asian people. Rice provides the bulk daily calories for humans and animals. One of the problems in SRI method is weed. Many efforts to control weed in the SRI method and one of the way island flooding before planting. The objective of the research was to study the land flooding effect before planting in controlling weed to yield components of several rice varieties. The research was conducted in farmer land of Kuranji and

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“…The grain counts per panicle at the mature stage are critical data for rice breeding research and yield assessment [ 1 , 2 ]. Additionally, it is viewed as one of the key traits for genetic improvement of rice yield [ 3 , 4 , 5 ]. Therefore, detection of the number of grains per panicle of rice is of great importance.…”

Section: Introductionmentioning

confidence: 99%

Automated Counting Grains on the Rice Panicle Based on Deep Learning Method

Deng

Tao

Huang

et al. 2021

Sensors

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Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. In this research, a grain detection model was proposed to automatically recognize and count grains on primary branches of a rice panicle. The model used image analysis based on deep learning convolutional neural network (CNN), by integrating the feature pyramid network (FPN) into the faster R-CNN network. The performance of the grain detection model was compared to that of the original faster R-CNN model and the SSD model, and it was found that the grain detection model was more reliable and accurate. The accuracy of the grain detection model was not affected by the lighting condition in which images of rice primary branches were taken. The model worked well for all rice branches with various numbers of grains. Through applying the grain detection model to images of fresh and dry branches, it was found that the model performance was not affected by the grain moisture conditions. The overall accuracy of the grain detection model was 99.4%. Results demonstrated that the model was accurate, reliable, and suitable for detecting grains of rice panicles with various conditions.

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A Rice Transcription Factor Controls Grain Length through Cell Number

Abstract: Yield-related QTLs and their applications in rice genetic improvement.

Cited by 4 publications

References 12 publications

Optimizing Silicon Application to Improve Growth, Grain Yield, and Nutrient Uptake of indica Rice (Oryza sativa cv. Bw 367)

Optimizing Silicon Application to Improve Growth, Grain Yield, and Nutrient Uptake of indica Rice (Oryza sativa cv. Bw 367)

Land Flooding Effect Before and After Planting on Rice Yield in the System of Rice Intensification

Automated Counting Grains on the Rice Panicle Based on Deep Learning Method

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