## 3 Analysis of Original Research Articles
### Deep Learning
[39] introduced a decomposition-reconstruction-ensemble framework with PSO and CS algorithms, improving forecast accuracy, relying on specific datasets. [PERSON] et al. [40] proposed the Bi-DSConvLSTM-Attention model, which integrated BiLSTM and DSConvLSTM with an attention mechanism, demonstrating significant accuracy improvements, but with limited discussion on generalization. [PERSON] et al. [41] incorporated spatial-temporal factors into an AttLSTM-ARIMA-BP model for corn price prediction in Sichuan Province, showcasing its efficacy in regional applications. [PERSON] and [PERSON] [42] proposed a hybrid model using K-means clustering, HANTS, and MLPNN for Thai rice price forecasting. Their model achieved superior accuracy (RMSE = 14.37, MAPE = 4.09%) compared to four benchmark methods, namely ARIMA, EMD-ARIMA, ANFIS, and persistence models. Explainable and event-driven models have also gained traction. [PERSON] et al. [43] introduced the CEEMDAN-CNN-JADE-TFT (temporal fusion transformer) model for corn futures, achieving high accuracy and interpretability. [PERSON] et al. [44] applied SCINet to predict Chinese corn futures prices, which outperformed LSTM, GRU, and TCN--even more so when enhanced with causally related economic indicators like soybean prices and exchange rates. [PERSON] et al. [45] developed a neural network model integrating real-world events from news articles with historical data to forecast prices for multiple commodities, achieving superior accuracy and outperforming all other linear and nonlinear models, but it relied on event extraction precision. [PERSON] and [PERSON] [46] enhanced short-term forecasts by incorporating futures prices into neural network models for daily corn cash prices and found that the NAR (nonlinear autoregressive) univariate model led to lower RMSEs benchmarked against a naive (no-change) model and a linear autoregressive (LAR) model, and the bivariate NAR model further improved results. In a different study, [PERSON] and [PERSON] [47] demonstrated the effectiveness of NN models over ARIMA benchmark models for capturing long-term trends. Advanced architectures and innovative data inputs have further improved prediction accuracy. [PERSON] et al. [48] utilized CNNs with aerial imagery for wheat futures, highlighting the potential of visual data; their model produced greater PNL than the SVM and MLP (multilayer perceptron) comparison models. [PERSON] et al. [49] combined LSTM and CNN to outperform other models in forecasting weekly grain prices, incorporating weather and macroeconomic factors to boost performance, with the inclusion of the snow factor for the first time in commodity price forecasting. [PERSON] et al. [50] leveraged Autoencoders and Variational Autoencoders with satellite-derived GPP data for corn yield and price predictions, though regional variability posed challenges. [PERSON] et al. [51] proposed a Bezier curve-enhanced LSTM model for predicting corn futures prices. The hybrid model outperformed traditional LSTM, ARIMA, VMD-LSTM (variational mode decomposition), and SVR, achieving the lowest RMSE (30.42), MAPE (0.80%), and Dstat (0.606). A Deep LSTM model was developed by [PERSON] et al. [52] to forecast international monthly corn and palm oil prices. The model outperformed ARIMA and TDNN models, having the lowest RMSE, MAPE, and MAD and the best directional accuracy. In another study, [PERSON] et al. [53] compared LSTM-RNNs to classical econometric time series models for forecasting corn futures prices. LSTM consistently outperformed traditional models,particularly at longer horizons, but it required careful preprocessing to capture seasonality and trends. [PERSON] et al. [54] proposed the \"RANC Crop Recommendation Tool,\" a crop recommendation and price forecasting model using DNNs, achieving over 90% accuracy, and it achieved better results than NN and Naive Bayes comparison models, but specific metrics for price forecasting were not detailed separately. [PERSON] et al.