Prediction of the impact of ecological restoration technology on the restoration of heavy metal pollution in agricultural soil

Heavy metal in agricultural soils increases its contamination and infertility in a due course of time. The vast expansion of industrialization along the agricultural soils has led to such adverse effects impacting vegetation. Several existing learning techniques are utilized to analyze the contamination in agricultural soil; however, the learning process consumes a high restoration error rate. This article introduces a Concentration-specific Restoration Prediction Technique (CRPT) for heavy metal-exposed agricultural soils. This technique relies on the heavy metal level present in different soil types for assessing its adverse impact. The vegetation failures and infertility progressions are accounted for by the continuous recurrent learning for predicting its restoration age. The restoration procedures preferred in the earlier stages are used for training the recurrent neural network. Based on the more feasible restoration method, the failure and infertility are validated for achieving a maximum prediction value. The learning process accounts for soil type, crop resistance, and climatic factors for holding restoration procedures. Therefore, the learning process performs restoration prediction along with resistance analysis for progressive restoration prediction. The least possible prediction value is validated using different training inputs for matching outputs. This technique is reliable in detecting pollution possibility with maximum restoration prediction (9.41%) and less training error (14.53%).