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.
Keywords: Biomass; carbon sequestration; climate change; food safety; heavy metals; sensors.
The review article focuses on recent applications of various artificial intelligence techniques and remote sensing approaches to monitoring plant growth as well as relevant morphological parameters using novel sensors, cameras, and associated modern technologies. Novel sensing techniques include multispectral sensors, hyperspectral sensors, infrared thermal imaging, RGB, LiDAR, fluorescence sensors, and UV sensors. Various measurement techniques are also discussed; these include near-infrared (NIR), NIR hyperspectral imaging, RGB, vis-NIR, Hyperspectral imaging, X-ray computed tomography (XRCT), MRI, and SAR. A brief study has also been done on the various application of phytoremediation potential in different contaminated soil. The opportunities, limitations, and future prospects of the AI technique in phytoremediation have been briefly discussed. However, an extensive study needs to be done on the application of AI on phytoremediation such as on heavy metal sequestration in plants, the effect of climate change on food security, carbon capture, etc. The efficiency and accuracy of data processing need to be more precise and improved from data acquisition. This article highlights the potential application of artificial intelligence in monitoring mobility, bioavailability, seasonal variation, the effect of temperature on plant growth, and plant response to the heavy metals in soil. The present review article can also help the readers/researchers to get a brief idea about the research gap and future perspective on the field of phytoremediation along with the application of various AI techniques.