@article{Ma2023, 
author = {Xiao Ya Ma and Jin Tong and Fei Jiang and Min Xu and Li Mei Sun and Qiu Yan Chen},
title = {Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain},
year = {2023},
journal = {Computers, Materials & Continua},
volume = {74},
number = {3},
pages = {6145-6159},
keywords = {Internet of things, intelligent agricultural supply chain, deep learning, production prediction},
url = {https://www.sciopen.com/article/10.32604/cmc.2023.034833},
doi = {10.32604/cmc.2023.034833},
abstract = {Production prediction is an important factor influencing the realization of an intelligent agricultural supply chain. In an Internet of Things (IoT) environment, accurate yield prediction is one of the prerequisites for achieving an efficient response in an intelligent agricultural supply chain. As an example, this study applied a conventional prediction method and deep learning prediction model to predict the yield of a characteristic regional fruit (the Shatian pomelo) in a comparative study. The root means square error (RMSE) values of regression analysis, exponential smoothing, grey prediction, grey neural network, support vector regression (SVR), and long short-term memory (LSTM) neural network methods were 53.715, 6.707, 18.440, 1.580, and 1.436, respectively. Among these, the mean square error (MSE) values of the grey neural network, SVR, and LSTM neural network methods were 2.4979, 31.652, and 2.0618, respectively; and their R values were 0.99905, 0.94, and 0.94501, respectively. The results demonstrated that the RMSE of the deep learning model is generally lower than that of a traditional prediction model, and the prediction results are more accurate. The prediction performance of the grey neural network was shown to be superior to that of SVR, and LSTM neural network, based on the comparison of parameters.}
}