New strategies on the application of artificial intelligence in the field of phytoremediation

Abstract

Artificial Intelligence (AI) is expected to play a crucial role in the field of phytoremediation and its effective management in monitoring the growth of the plant in different contaminated soils and their phenotype characteristic such as the biomass of plants. This review focuses on recent applications of various AI techniques and remote sensing approaches in the field of phytoremediation to monitor plant growth with relevant morphological parameters using novel sensors, cameras, and associated modern technologies. Novel sensing and various measurement techniques are highlighted. Input parameters are used to develop futuristic models utilizing AI and statistical approaches. Additionally, a brief discussion has been presented on the use of AI techniques to detect metal hyperaccumulation in all parts of the plant, carbon capture, and sequestration along with its effect on food production to ensure food safety and security. This article highlights the application, limitation, and future perspectives of phytoremediation in monitoring the mobility, bioavailability, seasonal variation, effect of temperature on plant growth, and plant response to the heavy metals in soil by using the AI technique. Suggestions are made for future research in this area to analyze which would help to enhance plant growth and improve food security in long run.