Zheng Chunwu, Li Huwei
Henan Economy and Trade Vocational College, Zhengzhou, China.
PeerJ Comput Sci. 2023 Apr 5;9:e1304. doi: 10.7717/peerj-cs.1304. eCollection 2023.
Smart agriculture can promote the rural collective economy's resource coordination and market access through the Internet of Things and artificial intelligence technology and guarantee the collective economy's high-quality, sustainable development. The collective agricultural economy (CAE) is non-linear and uncertain due to regional weather, policy and other reasons. The traditional statistical regression model has low prediction accuracy and weak generalization ability on such issues. This article proposes a production prediction method using the particle swarm optimization-long short term memory (PSO-LSTM) model to predict CAE. Specifically, the LSTM method in the deep recurrent neural network is applied to predict the regional CAE. The PSO algorithm is utilized to optimize the model to improve global accuracy. The experimental results demonstrate that the PSO-LSTM method performs better than LSTM without parameter optimization and the traditional machine learning methods by comparing the RMSE and MAE evaluation index. This proves that the proposed model can provide detailed data references for the development of CAE.
智慧农业可以通过物联网和人工智能技术促进农村集体经济的资源协调和市场准入，并保障集体经济的高质量、可持续发展。由于区域天气、政策等原因，集体农业经济（CAE）具有非线性和不确定性。传统的统计回归模型在这类问题上预测精度低、泛化能力弱。本文提出一种使用粒子群优化-长短期记忆（PSO-LSTM）模型预测集体农业经济的生产预测方法。具体而言，将深度循环神经网络中的长短期记忆方法应用于预测区域集体农业经济。利用粒子群优化算法对模型进行优化，以提高全局精度。实验结果表明，通过比较均方根误差（RMSE）和平均绝对误差（MAE）评估指标，PSO-LSTM方法比未进行参数优化的长短期记忆方法以及传统机器学习方法表现更好。这证明所提出的模型可以为集体农业经济的发展提供详细的数据参考。
