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.
The supply chain is an important pillar supporting the development of national economies, and the Internet of Things (IoT) is a key component of the information technology (IT) infrastructure that plays a major role in the development of an intelligent supply chain [
For an overall lower level of informatisation, digitisation, and intelligence [
Deep learning is a new research direction in the field of machine learning. As an emerging technology, to date, there have been few applied research achievements in the supply chain field. The demand forecasting of supply chain logistics is a major deep learning application in the supply chain field, covering production and sales forecasting [
Although science and technology have endowed the agricultural supply chain with better development, the agricultural supply chain has new risks in this IoT era, including perceived, network, and application-layer risks [
Our study focused on the production of an agricultural product supply chain in the field of agricultural output prediction. To explore the application of deep learning to the yield prediction problem and achieve results that would enrich the research on agricultural output prediction, we attempted to use deep learning for prediction by considering the local characteristics of the Shatian pomelo fruit yield. A comparison between this method and traditional prediction methods showed contradictory results.
The research results verified the superiority of deep learning in terms of the model generalisation ability and accuracy of the prediction results in comparison to traditional prediction methods applied to agricultural yield prediction. Three deep learning methods, namely, a grey neural network, support vector regression (SVR), and long short-term memory (LSTM) neural network, were used to predict the annual output of Shatian pomelo, which enriched the research achievements of deep learning in the field of agricultural product supply chain production.
The remainder of this paper is organised as follows. Section 2 provides a detailed literature review. Section 3 introduces the research methods. Section 4 includes the data analysis results and discussion. Section 5 describes the future scope of this research and provides some concluding remarks.
We conducted separate searches using subject keywords, including professional terms such as ‘Internet of things’, ‘deep learning’, ‘intelligent supply chain’, ‘agriculture supply chain’, and ‘supply chain’ in databases that included the Web of Science, Emerald, the Wiley Online Library, Taylor & Francis Online, Sage, and Springer Link. When no documents containing all the subject words or keywords were retrieved, the relevance ranking of the subjects or keywords was set for the document results retrieved from each database. We reviewed the paper titles and abstracts presented in the search results and selected manuscripts related to this study. We read the full text of each article selected by title and abstract. We then evaluated the quality of the articles, and finally determined the relevant studies.
As depicted in
Agricultural production is an important indicator for assessing the level of crop cultivation. The existing research on agricultural production forecasting can be generally divided into two categories: traditional forecasting methods based on linear fitting, and machine learning prediction methods that utilise nonlinear fitting. Among these two types of forecasting models, the grey forecasting model is the most commonly applied approach in agricultural production forecasting and demand forecasting. Cao [
The comparison of multiple individual prediction models is a common research method in agricultural production prediction studies [
In general, the development trend for the research on agricultural production forecasting is similar to that of price forecasting [
In an intelligent agricultural product supply chain, the target output forecast for an agricultural product is an important basis for estimating the cost budget and decision-making regarding the packaging, logistics, and transportation of that agricultural product. In addition, a forecast of the agricultural product output is applied in the production field at the beginning of the supply chain, which has a domino effect on all the links in the middle and lower reaches of the supply chain.
Based on the results of a systematic literature review, there have been few applied research results in the supply chain field. In addition to a monitoring function based on image recognition and image processing, another significant deep learning application in the supply chain field is supply chain demand prediction [
With further research, more emerging technologies and algorithms are gradually being integrated into the development of intelligent agricultural product supply chains [
At present, the research on the incorporation of deep learning to produce an intelligent agricultural product supply chains mainly focuses on three fields. (1) In the production and circulation fields of an intelligent agricultural product supply chain, image detection and image classification based on deep learning can assist in decision-making for the supply chain [
Previous studies have mostly focused on the selection of a certain type of prediction model. With the deepening of research, the focus on model selection for prediction has gradually shifted from traditional prediction models to econometric models, and thereafter to deep learning prediction models. Although previous scholars have compared different forecasting models, their applications were mostly concentrated in the field of price forecasting, and generally only two or three models were compared. In addition, few scholars have applied multiple types of yield forecasting models to a specific agricultural product as a joint comparative study. Thus, this study compared the yield prediction results of multiple prediction models for the Shatian pomelos yield. The models used in this comparison were representative models selected based on previous research.
The learning process of the back propagation (BP) neural network algorithm combines forward and back propagation. The structure of neural network model is shown in
The input layer, hidden layer, and output layer are the components of a basic neural network. The layers are connected by weights, and the output is calculated using the activation function, which mainly includes the following steps [
Step 1:
The number of network input layer nodes (
Step 2:
Step 3:
Step 4:
Step 5:
During each iteration of the gradient descent, parameters
Given a set of data,
The SVR model is formulated as follows [
LSTM is a variant of a recurrent neural network (RNN), where the difference between the two indicates the structure of the cells and operations. LSTM solves the phenomenon of a gradient disappearance or gradient explosion by introducing a ‘gate’ mechanism [
The LSTM network introduces a new internal state,
Here,
The weight matrix,
The input gate
The input gate weight matrix
The influence of the
The yield and trend prediction results for agricultural products are evaluated using various indicators. The mean square error (MSE) and root mean square error (RMSE) are used to evaluate the prediction error, where a smaller value indicates a higher prediction accuracy. The RMSE can be used to compare the advantages and disadvantages of different prediction models. The R value, which is a correlation coefficient, is an indirect measure of the goodness of fit between the actual and predicted values. For a prediction model, an R value that is closer to one is better.
The data used in this study were obtained from the
The fruit industry occupies an important position in China’s agricultural industry. A scientific and accurate prediction of fruit production is of great significance to the development of the fruit industry. Shatian pomelo is one of the local characteristic fruits of China and is an important representative of the developing regional economy. Forecasting the future Shatian pomelo output and regulating its planting based on the forecast data would assist in ensuring the market supply, reducing the supply chain costs, meeting consumer demand, and reducing waste. Moreover, this would provide positive guidance in maximising the beneficial transformation of characteristic fruits. Forecasting Shatian pomelo production would be extremely beneficial. Therefore, the annual regional Shatian pomelo yield was selected as the experimental data.
In the experimental study, deep learning prediction was conducted using Tubes-Bitki0LC, running on a 1.60-GHz Intel(R) Core(TM) I5-8265U CPU processor @1.80 GHz, with 8.00 GB (7.82 GB available) of installed memory, and a 64-bit Windows 10 operating system under a MATLAB 2021b environment with its own neural network toolbox.
In this study, regression analysis, quadratic exponential smoothing, grey prediction model, grey neural network, SVR, and LSTM methods were used to predict the production of Shatian pomelo. Among these, the regression analysis, quadratic exponential smoothing method, and grey prediction model are traditional prediction approaches, while the grey neural network model, SVM, and LSTM are deep learning prediction models. This group of prediction methods was selected based on a systematic literature review to determine methods that could be used for agricultural product output prediction. These included a traditional prediction method, single neural network prediction model, and neural network combined prediction model (grey neural network). Moreover, among the various methods, more classical specific prediction approaches were selected. After determining the traditional prediction method, we considered the influences of contrast and diversity on the selection of a deep learning prediction approach. Instead of selecting a combined prediction model with multiple deep learning prediction algorithms, we selected a single deep learning prediction model. This method was selected based on its representativeness, which was one of the key elements used to make the research more representative.
As listed in
Prediction model | Regression analysis | Exponential smoothing method (quadratic) | Grey forecasting model | Grey neural network | Support vector regression | Long short-term memory neural network |
---|---|---|---|---|---|---|
RMSE | 53.715 | 6.708 | 18.440 | 1.580 | 5.626 | 1.436 |
Prediction model | Grey neural network | Support vector regression | Long short-term memory neural network |
---|---|---|---|
Mean square error | 2.4979 | 31.652 | 2.0618 |
R | 0.99905 | 0.94 | 0.94501 |
The prediction results based on grey neural network and LSTM neural network are shown in
Year | 2022 | 2023 | 2024 | 2025 | 2026 |
---|---|---|---|---|---|
Grey neural network | 106.52 | 106.13 | 100.75 | 96.51 | 86.53 |
Long short-term memory neural network | 49.57 | 58.08 | 58.98 | 59.73 | 60.31 |
With ongoing research, newer prediction algorithms have been introduced, while the traditional prediction methods face various challenges. Based on the Shatian pomelo yield data, this study compared the prediction performances of different deep learning prediction models with those of several traditional prediction methods, and the universal superiority of a deep learning algorithm in agricultural production forecasting was verified according to the test parameters.
Data-driven agricultural supply chains pose challenges for data collection and visualisation. The similarity between the properties of the model and sample form of the dataset must be considered when using a single model for data prediction. The hyper-parameter settings of the deep learning neural network model applied also require heavy debugging. The hyper-parameter settings of the algorithm used in the neural network model can considerably influence the prediction results. To achieve the optimal prediction results, it is necessary to repeatedly adjust the relevant parameters to reduce loss value, reduce error, and improve accuracy.
In addition, although the accuracy of a deep learning prediction model is higher than that of a traditional prediction method, different deep learning prediction models produce different prediction results for the same event, as depicted in
The research on the use of a deep learning method for an intelligent agricultural supply chain under an IoT environment has indicated that some studies applied deep learning to the prediction of demand in an agricultural supply chain, including quantity prediction, indicating that deep learning is suitable for the yield prediction of agricultural products.
This study focused on the prediction of an agricultural product yield, using the annual yield of Shatian pomelo as the experimental data. We attempted to explore the influence and performances of various methods for predicting the agricultural product output, including a traditional prediction method dominated by linear fitting and a deep learning prediction algorithm dominated by nonlinear fitting. The experimental results demonstrated the following. (1) The RMSE values of regression analysis, quadratic exponential smoothing, grey prediction model, grey neural network, SVR, and LSTM network methods were 53.715, 6.708, 18.440, 1.580, 5.626, and 1.436, respectively. (2) The MSE test parameters of the grey neural network, SVR, and LSTM 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 also demonstrated the following. (1) The RMSE of a deep learning model was generally smaller than that of a traditional prediction model, which indicated the higher accuracy of the prediction results of the former model. (2) After comparing the test parameters (i.e., the MSE and R values) of the grey neural network prediction model, SVR, and LSTM network, it could be concluded that the prediction performance of the grey neural network was the best, whereas the performance of the LSTM method was the second best. (3) Based on the annual Shatian pomelo output data, the grey neural network prediction model and LSTM neural network prediction model, which achieved high accuracies, were considered for predicting the Shatian pomelo output for the next five years (i.e., 2022–2026). Between them, the Shatian pomelo output data predicted by the grey neural network were generally more accurate than the data predicted by the LSTM method.
Based on the results obtained, the R value indicated that the model had moderate accuracy. A higher R value indicates a greater similarity between the model and data. Under a condition where the MSE values of the two models were not significantly different, the prediction results of the grey neural network prediction model could be considered to be more accurate. Therefore, we believe that the Shatian pomelo supply chain can be planned and that decision-making can be achieved based on the data predicted using a grey neural network. In other words, to reduce costs and improve the efficiency, the cost input can be derived from the predicted output in reverse or from the work input required for the subsequent supply chain node operation under the forward planning forecast output. By considering the influence of the two parameters comprehensively, it would be possible to sum the prediction results of the two models after experts make proportional assignments.
Forecasting the target output of an agricultural product is helpful in reducing logistics costs and improving the operation efficiency of the supply chain during production, as well as the circulation and sale of the agricultural product within the supply chain. An accurate prediction for an agricultural output is of great significance to agriculture, agricultural product logistics, and the supply chain. Therefore, in this study, three traditional prediction methods and three deep learning prediction models were used to predict the yield of Shatian pomelo. Through research and an experimental comparison, it could be concluded that the accuracy of the yield prediction results when using deep learning were generally superior to those of other traditional prediction methods. In addition, the prediction results of the grey neural network and LSTM neural network prediction model were superior to those of SVR in this experiment.
The theoretical contribution of this study lies in the comparison of the performances of a traditional prediction model and deep learning prediction model in predicting the yield of a characteristic fruit based on examples. A grey neural network was found to be superior to a single neural network model in the yield prediction of the characteristic fruit. In terms of research methods, different prediction models were compared with specific yield data, and more effective and persuasive prediction results were provided.
By considering Shatian pomelo as an example, this research focused on the production forecasting of intelligent agricultural product supply chains under the background of an IoT, and expanded the application research field of deep learning. The output forecast results provide reference information for other nodes in the supply chain, making it possible to avoid the waste caused by the blind operation of the back-end links of the supply chain, predicted the demand for cold chain logistics of agricultural products in Hunan Province based on a sliding unbiased grey model.
However, this study had certain limitations. Although a deep learning method was introduced to predict the yield of Shatian pomelo and used the prediction model architectures of three algorithms, the combined prediction performance of different neural network prediction architectures under deep learning was not considered. Individual deep learning prediction models have their own advantages and disadvantages. There is still room for further improvement in the prediction accuracy using a combined prediction model with the advantages of independent models. At present, in the field of prediction research based on deep learning, producing the optimal combination model by integrating multiple deep learning algorithms has become a research trend. The combination and optimisation of different models for improving the accuracy of agricultural product forecasting can be a direction for future production forecasting prediction research into an intelligent agricultural supply chain.
We are grateful to all of those who provided useful suggestions for this study.