Application of artificial neural network to predict the environmental impacts of wheat cultivation systems

Document Type : Research Paper


1 Ph.D. student, Department of biosystem engineering, Takestan Branch, Islamic Azad University, Takestan, Iran

2 Associate Professor, Department of biosystem engineering, Takestan Branch, Islamic Azad University, Takestan, Iran

3 Assistant Professor Department of biosystem engineering, Takestan Branch, Islamic Azad University, Takestan, Iran


This study was conducted to analyze and model the environmental impacts using artificial neural networks (ANNs) in wheat production systems. Information needed for this study, related to 2021-2022, data was collected from wheat farms in two parts of conventional and conservation cultivation in Qazvin province, Iran. Life cycle assessment using the ReCiPe 2016 method reported three categories of damage to human health, ecosystem, and resources. The resource damage category for conventional tillage irrigation (76.05 USD2013) has significant pollution. The share of seed emissions, On-Farm emissions, and nitrogen emissions affect the categories of damage to human health, ecosystems, and resources, respectively. The results of ANN for environmental impacts in different wheat production showed the structure 9-8-3 with nine inputs, one hidden layer with eight neurons, and three output parameters have been determined as the best structure for conventional tillage irrigation. Also, rainfed wheat cultivation in conventional tillage showed 6-11-3 with six inputs, one hidden layer with eleven neurons, and three output parameters determined as the best structure. The best structure for irrigated cultivation of conservation tillage is 9-6-3. The suitable structure for rainfed cultivation in conservation tillage is 8-4-3, which has one hidden layer with four neurons. According to the results, the ANN can accurately predict the environmental effects of wheat production. By modeling the environmental effects, it can be found that in the future, sustainable production, will have a suitable plan to reduce environmental pollutants.


Main Subjects

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