BackgroundThere is much interest in how roots can be manipulated to improve crop performance in a changing climate, yet root research is made difficult by the challenges of visualising the root system accurately, particularly when grown in natural environments such as soil. Scientists often resort to use of agar- or paper-based assays, which provide unnatural growing media, with the roots often exposed to light. Alternatives include rhizotrons or x-ray computed tomography, which require specialist and expensive pieces of equipment, not accessible to those in developing countries most affected by climate change. Another option is excavation of roots, however, this is time-consuming and near impossible to achieve without some degree of root damage. Therefore, new, affordable but reliable alternatives for root phenotyping are necessary.ResultsThis study reports a novel, low cost, Rootrainer-based system for root phenotyping. Rootrainers were tilted at an angle, in a rhizotron-like set-up. This encouraged root growth on the bottom plane of the Rootrainers, and since Rootrainers open (in a book-like fashion), root growth can be easily observed. This new technique was successfully used to uncover significant genotypic variance in rooting traits for a selection of lettuce (L. sativa) varieties across multiple timepoints.ConclusionThis novel Rootrainertron method has many advantages over existing methods of phenotyping seedling roots. Rootrainers are cheap, and readily available from garden centres, unlike rhizotrons which are expensive and only available from specialist suppliers. Rootrainers allow the roots to grow in substrate medium, providing a significant advantage over agar and paper assays.This approach offers an affordable and relevant root phenotyping option and makes root phenotyping more accessible and applicable for researchers.

Phenotyping yam (Dioscorea spp.) germplasm for resistance to parasitic nematodes is hampered by the lack of an efficient screening method. In this study, we developed a new method using rooted yam vine cuttings and yam plantlets generated from semi-autotrophic hydroponics (SAHs) propagation for phenotyping yam genotypes for nematode resistance. The method was evaluated using 26 genotypes of D. rotundata for their reaction to Scutellonema bradys and four root-knot nematode species, Meloidogyne arenaria, M. enterolobii, M. incognita, and M. javanica. Yam plantlets established in nursery bags filled with steam-sterilized soil were used for screening against single nematode species. Plants were inoculated four weeks after planting and assessed for nematode damage eight weeks later. A severity rating scale was used to classify genotypes as resistant, tolerant, or susceptible determine based on the nematode feeding damage on tubers and the rate of nematode multiplication in the roots of inoculated plants. The results demonstrated putative resistance and tolerance against S. bradys in 58% of the genotypes and 88%, 65%, 65%, and 58% against M. arenaria, M. javanica, M. incognita, and M. enterolobii, respectively. The method is rapid, flexible, and seasonally independent, permitting year-round screening under controlled conditions. This method increases the throughput and speed of phenotyping and improves the selection process.

Amaranth (Amaranthus spp.) is a significant leafy vegetable and cereal crop with high nutrient benefits that is widely consumed worldwide. To maximise its yield, farmers massively rely upon synthetic fertilisers to enhance the quality of the crop. However, this obsessive usage of inorganic fertiliser leads to severe ecosystem damage. For agricultural and ecological sustainability, it is essential to comprehend the process underlying this environmental degradation. This paper analyses the effect of inorganic fertilisers on the growth and yield of Amaranthus. By identifying Amaranthus's productivity and adaptability in different chemically treated soil conditions and automatically phenotyping its traits using image-based deep learning models, this study aims to determine the overuse of synthetic fertilisers. A comparative evaluation of different state-of-art CNN models was carried out, and the experimental result proves that DenseNet-121 could be a more appropriate learning algorithm for the proposed system with 84% accuracy. It is believed that the proposed deep learning based automated phenotyping framework could greatly assist farmers in understanding the actual requirement of soil, thus avoiding the residual impact of fertiliser abuse in the environment.