Organic agricultural food web population iteration model based on machine learning optimization

This work develops a population iteration model of the agricultural food web based on long short-term memory network optimization to enhance the predictive accuracy of farmland ecosystem species dynamics. The model integrates classical ecological modeling with machine learning technology to represent the nonlinear correlation among crops, weeds, insects and birds, and to take into consideration the influence of periodic changes and human activity. The experimental results indicate that the prediction error of the LSTM model across different agricultural conditions is significantly reduced compared with the classical model, particularly the biological control scenario, with greater ecological benefits. The work provides novel tools to achieve sustainable development of organic agriculture and provides a scientific basis to support ecological management and production optimization.