An overview of image-based phenotyping as an adaptive 4.0 technology for studying plant abiotic stress: A bibliometric and literature review

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.

Interaction of Genetic and Cultivation Technology in Maize Prolific and Productivity Increase

<b>Background and Objective:</b> Maize cultivation technology package development is a solution in increasing maize production, especially prolific maize. However, technology package evaluation has to be evaluated with interaction towards crop genetics. The purpose of this research is to discover the interaction between maize variety towards cultivation technology (plant fertilization and spacing) and to find information about secondary production characters in cultivation technique optimization. <b>Materials and Methods:</b> This research used a split-split-plot design. The main plot was planting system (S) consisted of three planting systems. Sub Plot (SP) was fertilizing plans ha<sup>1</sup> (P) consisted of four plans. Whereas Sub-Sub Plot (SSP) were (V): NASA 29 (V<sub>1</sub>), Bisi 2 (V<sub>2</sub>) and Sinha's 1 (V<sub>3</sub>). There were 15 characters observed. <b>Results:</b> The results prolific potential is very dynamic which is determined by genetic potential, cultivation technology and genetic-cultivation technology interactions. The increase in the prolific potential will have a direct effect on increasing maize productivity. In general, the use of legowo lines and Eco-farming (biofertilizer) can increase prolific potential and productivity. <b>Conclusion:</b> According to this research, the prolific potential is highly dynamic which is determined by genetic potential, cultivation technology and genetic-cultivation interaction. Technology considered in increasing maize productivity is Legowo plant spacing (50+100)×20 cm combined with N:P:K = 200:100:50+KNO<sub>3</sub> 25 kg ha<sup>1</sup>+Eco farming 5 cc L<sup>1</sup>. This technique combination is recommended in maize productivity increase.