Non-destructive phenotyping for early seedling vigor in direct-seeded rice

Abstract

Background Early seedling vigor is an essential trait of direct-seeded rice. It helps the seedlings to compete with weeds for water and nutrient availability, and contributes to better seedling establishment during the initial phase of crop growth. Seedling vigor is a complex trait, and phenotyping by a destructive method limits the improvement of this trait through traditional breeding. Hence, a non-invasive, rapid, and precise image-based phenotyping technique is developed to increase the possibility to improve early seedling vigor through breeding in rice and other field crops. Results To establish and assess the methodology using free-source software, early seedling vigor was estimated from images captured with a digital SLR camera in a non-destructive way. Here, the legitimacy and strength of the method have been proved through screening seven diverse rice cultivars varying for early seedling vigor. In the regression analysis, whole-plant area (WPA) estimated by destructive-flatbed scanner (WPAs) and non-destructive imaging (WPAi) approaches was strongly related (R2 > 83%) and suggested that WPAi can be adapted in place of destructive methods to estimate seedling vigor. In addition, this study has identified a set of new geometric traits (convex hull and top view area) for screening breeding lines for early seedling vigor in rice, which decreased the time by 80% and halved the cost of labor in data observation. Conclusions The method demonstrated here is affordable and easy to establish as a phenotypic platform. It is suitable for most glasshouses/net houses for characterizing genotypes to understand the plasticity of shoots under a given environment at the seedling stage. The methodology explained in this experiment has been proven to be practical and suggested as a technique for researchers involved in direct-seeded rice. Consequently, it will help in the simultaneous screening of genotypes in large numbers, the identification of donors, and in gaining information on the genetic basis of the trait to design a breeding program for direct-seeded rice.

An Automatic Method for Rice Seed Vigor Classification Via Radicle Emergence Test Using Image Processing, Curve Fitting and Clustering Method

“…In addition, the results are subjective as they are heavily dependent on the expertise of the tester. Therefore, seed scientists have been trying to nd a convenient, economical and standardized method for seed vigor testing [26]. Unfortunately, to date, there has been no report of an universally acceptable single indirect test for assessing seed vigor [6].…”

An Automatic Method for Rice Seed Vigor Classification Via Radicle Emergence Testing Using Image-Processing, Curve-Fitting and Clustering Methods

“…Therefore, seed scientists have been attempting to nd a convenient, economical and standardized method for seed vigor testing [32]. Unfortunately, to date, there has been no report of a universally acceptable single indirect test for assessing seed vigor [7].…”

“…Data were acquired using a laboratory-scale imaging system described by Joosen et al [32]. Brie y, a digital single-lens re ex camera (Nikon D5200 with Nikkor AF-S 60 mm f/2.8 G Micro ED; Nikon, http://www.nikon.com) was xed to a repro stand.…”

Improvement of Phosphorus Use Efficiency in Rice by Adopting Image-Based Phenotyping and Tolerant Indices

“…To overcome this difficulty, the advent of proximal sensing technology allows for critical non-destructive support in measuring performance and predicting crop yield under controlled and field environments (Araus and Cairns, 2014;Araus and Kefauver, 2018). A non-invasive technique, such as high-throughput image-based phenotyping, e.g., visible imaging or RGB (red-green-blue) imaging, has a potential application, as it screens the varieties at earlier stages, covering crucial parameters such as vegetative mass associated with important traits such as grain yield in cereals (Ghamkhar et al, 2019;Anandan et al, 2020). It allows the measurement of dynamic changes in the plant form over time and quantifies based on multiple parameters such as tillering, leaf area index, leaf angle, and convex hull (Anandan et al, 2020).…”

“…Consequently, the imaging technique is gaining momentum among researchers to screen genotypes for several biotic and abiotic factors such as salinity, nitrogen, water deficiency, nodal root angle, and early seedling vigour in barley, rice, and sorghum (Araus et al, 2012;Crowell et al, 2014;Atkinson et al, 2015;Turner et al, 2018;Narisetti et al, 2019;Wu et al, 2019;Anandan et al, 2020). However, the phenotypic screening of rice genotypes under low phosphorus conditions is not established.…”