The effects of different concentrations of cadmium and 2,2 ',4,4 ',5,5 '-hexabrominated diphenyl ether (BDE-153) on the growth and related physiological and biochemical indexes of Amaranthus mangostanus L. (amaranth) were studied. The results showed that the presence of BDE-153 promoted the absorption of Cd by the amaranth and inhibited the migration of Cd from the roots to the shoots. At the same time, 0.1 mg/L of Cd had a synergistic effect on the migration of BDE-153, but 5 mg/L Cd inhibited the accumulation of BDE-153 in the aboveground part of the amaranth. In addition, the kinetics of the uptake of pollutants by the amaranth showed that both Cd and BDE-153 could be transported by amaranth, but Cd and BDE-153 were mainly enriched in the roots, and the presence of Cd may cause a lag in the uptake of BDE-153 in the shoots. Compared with the control group, the biomass of the amaranth affected by BDE-153 and a high concentration of Cd (5 mg/L) decreased by 30.2-49.5%, the chlorophyll content decreased by 43.0-60.3%, the Evans blue increased, and the MDA content was higher. The activities of superoxide dismutase (SOD) and catalase (CAT) also decreased with an increase in the BDE-153 concentration. This indicates that the interaction between BDE-153 and a high concentration of Cd (5 mg/L) is more toxic to amaranth than single Cd pollution. This paper provides the necessary data support for phytoremediation of heavy metal and organic compound pollution.

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.