RESUMO
Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.
RESUMO
Stagnant flooding (SF) is a major problem in rainfed lowlands where floodwater of 25-50 cm stagnates in the field for most of the season. We aimed to establish a system for phenotyping SF tolerance and identifying tolerant germplasm through screening of landraces. A total of 626 rice accessions were evaluated over 3 years under control conditions and two levels of SF. Floodwater was raised to 20 cm at 25 or 30 days after transplanting (DAT). In one trial, the depth was increased subsequently by 5 cm a week and in another (severe stress), it was increased to 40 cm at 37 DAT and to 50 cm at 42 DAT. In both trials, water depth was maintained at 50-60 cm until maturity. In all cases, no plant was completely submerged. Plant height, elongation rate and yield were measured at maturity. Genotypes best suited to SF showed moderate elongation of 1.3-2.3 cm day(-1) under SF. In contrast, semi-dwarf and fast-elongating types performed poorly. Subsequent trials using 18 genotypes, including six pairs of near isogenic lines (NILs) with or without SUB1 showed that all SUB1 NILs were sensitive to SF. Five of the other six genotypes contained SUB1 and were SF tolerant, suggesting the possibility of combining tolerances to complete submergence (SUB1) and SF. Stem starch and soluble sugar concentrations were similar under control conditions among the 18 genotypes, but starch was depleted by 37 % under SF, with less depletion in tolerant genotypes. SUB1 NILs contained similar concentrations of starch and sugars under SF. We conclude that survival and yield under SF are dependent on moderate elongation, high tillering, lesser carbohydrate depletion and higher fertility. The tolerant genotypes identified here performed strongly in both wet and dry seasons and will be used to identify tolerance mechanisms and alleles for use in marker-assisted breeding.