Prediction of Salt-Tolerant Earthworms’ Cultivation Conditions Based on the Robust Artificial Intelligence Model

Mingyue Wang, Shengzhe Chu, Qiang Wei, Chunjie Tian, Yi Fang, Guang Chen () and Sitong Zhang ()
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Mingyue Wang: Country College of Life Science, Jilin Agricultural University, Changchun 131018, China
Shengzhe Chu: Country College of Life Science, Jilin Agricultural University, Changchun 131018, China
Qiang Wei: Country College of Life Science, Jilin Agricultural University, Changchun 131018, China
Chunjie Tian: Key Laboratory of Mollisols Agroecology, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Yi Fang: Key Laboratory of Mollisols Agroecology, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Guang Chen: Country College of Life Science, Jilin Agricultural University, Changchun 131018, China
Sitong Zhang: Country College of Life Science, Jilin Agricultural University, Changchun 131018, China

Sustainability, 2023, vol. 15, issue 8, 1-16

Abstract: Soil pollution is a very important field among current global ecological environmental problems. Many countries have focused their scientific research power on the process of soil remediation and biological detoxification, hoping to achieve the remediation effect of contaminated soil by means of biological free activity and survival mechanisms. These studies are meant to achieve a virtuous ecological cycle and provide a biological basis for the sustainable utilization and development of resources. The purpose of this study was: (1) to screen the best conditions for the cultivation and domestication of salt-tolerant earthworms; (2) to explore the influence (correlation) relationship between salt-tolerant earthworms’ growth variables and living environmental factors; (3) an improved BP neural network model was constructed to predict the expected values of variables such as C:N, N a H C O 3 : N a 2 C O 3 and base:soil, so as to provide an initial cultivation model for earthworm-resistant cultivators. The materials used in this study are cow dung that was collected from Changchun LvYuan District PengYu farm; straw that was collected from the Key Laboratory of Comprehensive Straw Utilization and Black Land Protection; soil that was collected from ordinary soil in the experimental shed of Jilin Agricultural University. We also purchased “Daping No. 2” earthworms from Hunan Zengren Earthworm Breeding Base. In order to simulate the extreme living environment with high salinity and alkalinity, this paper prepared 0.1 mol/L and 0.15 mol/L N a H C O 3 solution, 0.1 mol/L, and 0.2 mol/L N a 2 C O 3 solutions. We mixed the above solutions according to the proportion of 0.1 mol/L N a H C O 3 solution: 0.1 mol/L N a 2 C O 3 solutions, 0.15 mol/L N a H C O 3 solution: 0.1 mol/L N a 2 C O 3 solution, 0.1 mol/L N a H C O 3 solution: 0.2 mol/L N a 2 C O 3 solutions. At the same time, we prepared the mixed environment of base material and soil (base material:soil = 1:1; base material:soil = 1:2); the base material was composed of cow dung and straw. The conclusions are as follows: (1) earthworms living under simulated conditions have stronger tolerance to the saline-alkali environment; (2) the situation of C:N = 30:1, N a H C O 3 : N a 2 C O 3 = 1:1, base:soil = 1:2 is the ideal state for earthworms to survive; (3) earthworms with a high tolerance can provide more enzyme activities for the simulated environment, especially cellulase activity, urease activity, sucrase activity, and alkaline phosphatase activity; (4) compared with the ordinary practical operation, the average prediction accuracy of a three output neuron BP prediction model is 99.40% (>95%). The results of this study indicate that the BP neural training set established can be used to reduce breeding costs, and also to improve the productivity of earthworms, provide a mathematical model basis for ecological sustainable utilization and circular production between earthworms and soil, and rapidly encourage the ability of earthworms to repair contaminated soil or transform agricultural waste, providing basic data support conditions for soil ecological remediation systems and the sustainable utilization of agricultural waste.

Keywords: soil pollution; earthworms; NaHCO 3; Na 2 CO 3; BP (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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