Development of a drought stress-resistant rice restorer line through Oryza sativa–rufipogon hybridization

Size measurement and filled/unfilled detection of rice grains using backlight image processing

Measurements of rice physical traits, such as length, width, and percentage of filled/unfilled grains, are essential steps of rice breeding. A new approach for measuring the physical traits of rice grains for breeding purposes was presented in this study, utilizing image processing techniques. Backlight photography was used to capture a grayscale image of a group of rice grains, which was then analyzed using a clustering algorithm to differentiate between filled and unfilled grains based on their grayscale values. The impact of backlight intensity on the accuracy of the method was also investigated. The results show that the proposed method has excellent accuracy and high efficiency. The mean absolute percentage error of the method was 0.24% and 1.36% in calculating the total number of grain particles and distinguishing the number of filled grains, respectively. The grain size was also measured with a little margin of error. The mean absolute percentage error of grain length measurement was 1.11%, while the measurement error of grain width was 4.03%. The method was found to be highly accurate, non-destructive, and cost-effective when compared to conventional methods, making it a promising approach for characterizing physical traits for crop breeding.

Management and Utilization of Plant Genetic Resources for a Sustainable Agriculture

Despite the dramatic increase in food production thanks to the Green Revolution, hunger is increasing among human populations around the world, affecting one in nine people. The negative environmental and social consequences of industrial monocrop agriculture is becoming evident, particularly in the contexts of greenhouse gas emissions and the increased frequency and impact of zoonotic disease emergence, including the ongoing COVID-19 pandemic. Human activity has altered 70–75% of the ice-free Earth’s surface, squeezing nature and wildlife into a corner. To prevent, halt, and reverse the degradation of ecosystems worldwide, the UN has launched a Decade of Ecosystem Restoration. In this context, this review describes the origin and diversity of cultivated species, the impact of modern agriculture and other human activities on plant genetic resources, and approaches to conserve and use them to increase food diversity and production with specific examples of the use of crop wild relatives for breeding climate-resilient cultivars that require less chemical and mechanical input. The need to better coordinate in situ conservation efforts with increased funding has been highlighted. We emphasise the need to strengthen the genebank infrastructure, enabling the use of modern biotechnological tools to help in genotyping and characterising accessions plus advanced ex situ conservation methods, identifying gaps in collections, developing core collections, and linking data with international databases. Crop and variety diversification and minimising tillage and other field practices through the development and introduction of herbaceous perennial crops is proposed as an alternative regenerative food system for higher carbon sequestration, sustaining economic benefits for growers, whilst also providing social and environmental benefits.

Development and verification of SSR markers from drought stress-responsive miRNAs in Dongxiang wild rice (Oryza rufipogon Griff.)

Rice production worldwide has continued to decline due to various environmental stresses, with drought stress being a prominent factor, as rice is a semi-aquatic plant. Thus, development of drought stress-resistant rice varieties is of great importance for rice production. In our previous study, we found that microRNAs (miRNAs) play a crucial role in the response to drought stress in Dongxiang wild rice (DXWR) (Oryza rufipogon Griff.). Developing drought stress-responsive miRNA-based single sequence repeat (SSR) markers for DXWR will be of great value for the efficient identification and utilization of miRNA genes to breed drought stress-resistant rice varieties. In this study, ninety-nine novel SSR markers were developed based on the drought stress-responsive miRNAs of DXWR. These markers were distributed in all 12 rice chromosomes, and most were in chromosomes 2 and 6, with di- and tri-nucleotides being the most abundant repeat motifs. Twelve out of fourteen synthesized markers displayed high levels of genetic diversity in the genomes of three populations of DXWR and 40 modern rice varieties worldwide. The number of alleles per locus ranged from 2 to 7, with an average of 4.67; the genetic diversity index ranged from 0.21 to 0.76, with an average of 0.58; and the polymorphism information content value ranged from 0.18 to 0.72, with an average of 0.53. These novel molecular markers developed from the drought stress-responsive miRNAs of DXWR could be additional tools for mapping elite miRNA genes and breeding drought stress-resistant rice varieties.

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

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.