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Evaluation of four neural network models optimized based on sparrow search algorithm for predicting the stem thickness of Panax notoginseng
SHANG Xiao-jian, ZHANG Rui
HUBEI AGRICULTURAL SCIENCES
2024, 63 (8):
72-77.
DOI: 10.14088/j.cnki.issn0439-8114.2024.08.013
Taking 1-year-old Panax notoginseng as the research object, the effects of light, water, and nutrients on the stem diameter of Panax notoginseng were investigated through orthogonal experiments, sparrow search algorithm （SSA） was used to optimize four models, namely back propagation neural network （BPNN）, Long short term memory （LSTM）, random forest （RF）, and general regression neural network （GRNN）, and these four models were applied to predict the stem thickness of Panax notoginseng. The results showed that non-biological factors such as light, water, and fertilizer had a significant impact on the stem diameter of Panax notoginseng. The degree of influence of each factor on the stem diameter of Panax notoginseng was shading layer>soil moisture content>potassium fulvic acid content from mineral sources>light duration. The SSA-GRNN model had the highest coefficient of determination, which was 0.865 6, followed by the SSA-RF model, SSA-BPNN model, and SA-LSTM model;the MAE and MSE of the SSA-GRNN model were 0.064 1 and 0.008 7, respectively, which were lower than those of the SSA-BPNN model, SSA-LSTM model, and SSA-RF model;the fitness of SSA-RF model and SSA-LSTM model was relatively high, and they were trapped in local optima, making it impossible to achieve a global optimal solution. SSA-GRNN model had the lowest fitness and achieved the best fitness with the least number of iterations.
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