Comprehensive assessment of soil quality in greenhouse agriculture based on genetic algorithm and neural network - Sun, Yuemei; Zhang, Jingzhi; Bai, Jingbo; Xu, Yanxiang; Chen, Yunlin; Lan, Yubin; Han, Xin (Journal of soils and sediments, 2024)
Purpose With the continuous advancement of modern agriculture and urbanization, soil quality assessment has been considered an important guarantee for sustainable agricultural development. Despite the availability of numerous methods for assessing soil quality, little emphasis has been paid to comprehensive studies on soil quality in greenhouse agriculture. This study aims to construct a comprehensive evaluation model of greenhouse agricultural soil quality, including soil nutrition and heavy metal pollution, to better assess greenhouse soil quality. Materials and methods In this study, the concentrations of eight heavy metals, five soil nutrients, and nine soil-available microelements in 300 greenhouses were measured. Genetic algorithm–backpropagation (GA-BP) neural networks and backpropagation (BP) neural networks were used to construct a comprehensive soil quality evaluation model, and the soil quality of the greenhouse in the study area was evaluated based on soil nutrients and heavy metal pollution. Results The results showed that the prediction accuracy of both models exceeded 85%. However, constructed utilizing the genetic algorithm–backpropagation (GA-BP), the evaluation model can be more effective in assessing soil quality, with an accuracy of 96.1%. In this study, the soil quality was categorized into eight levels: IA, IB, IC, IIA, IIB, IIC, IIIA, and IIIB. 80.6% of the samples were IIA and IIB, suggesting that the soil quality of greenhouse planting sheds in this research area was poor, with severe heavy metal pollution, although soil nutrients were relatively sufficient. Conclusions This study holds significance for assessing soil quality in greenhouse agriculture and improving agricultural scientific management.