Using Artificial Intelligence Algorithms and Spatial Analysis of Agaricus bisporus in the Wilderness Near Lake Milh (Al-Razzaza), Iraq

Advanced applications of artificial intelligence and geographic information systems (GIS) techniques are used to monitor plant growth across their vegetation seasons using morphological parameters. This research presents novel measurements to determine the concentrations of elements such as carbon (C), nitrogen (N), hydrogen (H), lead (Pb), and cadmium (Cd) in the mushroom “Agaricus bisporus” and in the surrounding soil and air. These data are spatially analyzed to contribute to long-term predictions of pollution index and future ecosystem risks. Pollution and element accumulation in the mushroom, soil, and surrounding air were monitored using data accompanied by a geographic map. Pollution was assessed by transforming the system and adopting a methodology that integrates traditional methods with artificial intelligence, aiming to address the challenges with greater efficiency and accuracy. Input parameters were used to develop models using artificial intelligence and statistical methods to detect metal accumulation, and monitor carbon, hydrogen, nitrogen, and seasonal changes. The response of plants to heavy metals (lead and cadmium) in soil and air and their impact on their growth and development, were analyzed. The techniques showed a significant reduction in the error rate when using fungi as an indicator to predict dietary heavy metal concentrations, as the accuracy of artificial intelligence was remarkable in estimating the concentration of elements and their transfer from soil to plant. The integration of artificial intelligence, machine learning, and GIS technologies enhances environmental management, as it provides the ability to monitor, predict, and provide sustainable assessments. This study provides insights to improve plant growth, reduce pollution, and support long-term food security at a lower cost and with greater accuracy in assessing environmental impacts.