Using Artificial Intelligence Algorithms and Spatial Analysis of Agaricus Bisporus in the Wilderness near Lake Milh Al-Razzaza-Iraq

Abstract

Introduction: Advanced applications of artificial intelligence and GIS techniques have been in monitoring the plant growth throughout their vegetation seasons with morphological parameters. Goal: In this paper new measurements to determine the concentrations of carbon, nitrogen, hydrogen, lead and cadmium in Agaricus bisporus, and the soil and air around it are presented and analyzed spatially with data and hypotheses that aid in long term predictions of pollution index with Ecological Potential Ecological Risk. Materials and Methods: Monitor pollution and uptake of elements into mushrooms and the surrounding air and soil in Geo-referenced data mapped by monitoring it. It identifies and manages risks associated with long-term predictions for the future developing risk scenarios based on land cover analysis. Pollution assessment using the transformation of the system and methodology integrating traditional ways with AI. Trying to address these challenges in a more efficient and accurate manner in the future Results: The input parameters were used to develop models with the help of artificial intelligence and statistical methods. AI techniques detected excessive metal accumulation, carbon sequestration, hydrogen and nitrogen monitoring, and seasonal changes. The plant’s response to heavy metals (lead and cadmium) in soil and air affecting growth and development during the plant’s life cycle was analyzed. Recommendations for future research were use to promote plant growth, decrease pollution, and improve long-term food security. The lower the expenses and the higher the accuracy of the AI technology for determination. For the first time, when using mushrooms as a parameter for predicting nutrient heavy metals, the values of the error percentage are at a low value for training, testing, and rejecting data. Conclusions: Detailed artificial intelligence such as deep learning of neural networks accurately estimates concentration or transport of soil elements to plants among others. Integration of AI, machine learning, and GIS improves the environmental management because it provides capacity to monitor, predict and conduct sustainable opinions.