Abstract

Artificial intelligence (AI), together with environmental biotechnology, is a radical change in the real-time data of soil and water quality. The common limitations of traditional surveillance techniques include the fact that there is a long-time lag, and the techniques are prohibitively expensive, such as chemical analysis in the laboratory, and cannot respond to the dynamics of environmental pollutants. The hybrid framework suggested in the given research is based on the use of microbial biosensors, in particular, the microorganisms specially modified to release bioluminescent or electrochemical signals when in touch with contaminants, as the main units of detection. This layer of hardware is an Internet of Things (IoT) that takes these biological responses and forwards them to a Long Short-Term Memory (LSTM) neural network that analyzes complex time-series data. The system to provide high sensitivity under varying field conditions uses a statistical model, which uses non-linear saturation kinetics to calibrate the biological output with respect to the concentrations of certain contaminants. To narrow down on these predictions, a Generalized Linear Model (GLM) is used to sieve the environmental noise that is introduced due to changes in soil pH and temperature that tend to bias raw sensor values. Moreover, the Bayesian Inference algorithm is applied, which dynamically optimizes detection thresholds; hence, the system can learn and adapt to a particular site condition with time. This computational layer was found to work well in minimizing false-positive reporting by 22 %. As shown in experimental results, this combined methodology has a detection accuracy of 94.5 % for detecting heavy metals and nitrates and essentially reduces the analysis lead-time from 48 hours to a 15-minute time span. This system allows closing the divide that exists between biological sensing and computational intelligence, to offer a scalable engineering solution to autonomous environmental management as well as the development of Precision Remediation strategies in the agricultural and industrial sectors.