## 3 Analysis of Original Research Articles
### Deep Learning
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Refs.** & **Year** & **Commodity Name** & **Dataset** & **Technique** & **Findings** & **Limitation** \\ \hline \multirow{9}{*}{[52]} & \multirow{9}{*}{2021} & & Monthly price of & & DLSTM reduced & \\  & & & Corn and Palm oil & & RMSE, MAPE, and & Limited focus \\  & & & Corn and Palm oil & & MAD by 72\% & on model \\  & & & (1980–2020); & DLSTM, & compared to ARIMA & adaptability \\  & & & World Bank & TDNN, ARIMA & and 47\% compared to & to other \\  & & & Commodity & & TDNN; for corn: & commodities or \\  & & & Price Data & & RMSE 0.031, MAPE & time frames. \\  & & & & & 7.337\%, MAD 0.026 & \\ \hline \multirow{9}{*}{[53]} & \multirow{9}{*}{2024} & & & & LSTMs outperform & \\  & & & Daily corn futures & LSTM, VAR, ARIMA & & LSTMs failed & to capture \\  & & & & ARIMA & forecasts (7-day & seasonality \\  & & & & forecast horizon & and trends \\  & & & & MAE 2.75) & & \\ \hline \multirow{9}{*}{[54]} & \multirow{9}{*}{2023} & & & & Crop & Specific accuracy \\  & & & Soil test reports, & RANC & recommendation & for price \\  & & & crop yield data, & algorithm, & accuracy exceeded & forecasting not \\  & & & stock information & SVM, ANN, RNN, RBM & 90\%; price prediction & reported; \\  & & & & & component & challenges with \\  & & & & RNN, RBM & integrated into & real-time \\  & & & & & decision system & data integration \\ \hline \multirow{9}{*}{[55]} & \multirow{9}{*}{2023} & & & Hybrid & \\  & & & & SARIMA- & & MAPE: Wheat 5.37, & \\  & & & & data (2005–2022) & XGBoost, & Corn 7.80, Rice 6.87 & \\  & & & & ARIMA, & & hinder scalability \\  & & & & LSTM, SARIMA & & \\ \hline \multirow{9}{*}{[56]} & \multirow{9}{*}{2017} & & & & & VMD-PSO-BPNN & \\  & & & Daily Wheat, & & had the best accuracy; & \\  & & & Corn, Soybean & PSO-BPNN & for wheat & \\  & & & futures prices. & with & MAPE = 0.55\%, & \\  & & & (2010–2016); & Decomposition & MAE = 2.68, & \\  & & & Chicago Board & Methods & RMSE = 3.41, for & \\  & & & of Trade & & corr: MAPE = 0.57\%, & \\  & & & & MAE = 2.12, & \\  & & & & RMSE = 2.82 & & \\ \hline \multirow{9}{*}{[57]} & \multirow{9}{*}{2023} & & & Wheat price, & & \\  & & & freight volume, & & BP-LSTM model & \\  & & & turnover, express & & outperformed others & Limited dataset \\  & & & delivery, & (MSE = 0.00026); & (10 years); the \\  & & & consumer price & BP–LSTM, & captured complex & theoretical depth \\  & & & index, money & LSTM, BP & price dynamics using & of DL approach is \\  & & & related factors & & external factors like & still under \\  & & & (2012–2021); & & freight volume, & development \\  & & & Bureau of & & EPI, etc. & \\ \hline \multirow{9}{*}{[23]} & \multirow{9}{*}{2024} & & & Global wheat & ARIMA, & \\  & & & prices, & ARIMA, & SARIMA performed & Limited \\  & & & macroeconomic & ARIMA, & best; LSTM and & \\  & & & indicators & LSTM, CNN, & CNN overfit & \\  & & & Regression & training data & testing phase \\ \hline \multirow{9}{*}{[58]} & \multirow{9}{*}{2024} & & & ChineseBERT, & \\  & & & & Text CNN, & \\  & & & Ensemble & & \\  & & & Empirical Mode & & \\  & & & Decomposition, & 60-day horizon & \\  & & & & (MAPE of 3.42 and & \\  & & & MAE of 78.80), & & \\  & & & & NBEATSx, & \\  & & & XGBoost, LSTM & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Hybrid models incorporating optimization algorithms and external data sources have also shown promise. [PERSON] and [PERSON] [37] developed a GWO-CNN-LSTM model using the Baidu Index and Google Trends data, enhancing predictions with real-time insights, though limited in commodity diversity. [PERSON] et al. [38] applied an LSTM model to forecast prices of soybean, corn, and wheat in China. The model was trained using historical prices and optimized using time-based sliding windows. In comparative evaluations, LSTM achieved the lowest prediction errors across all three crops, outperforming traditional methods such as SVR and BP neural networks. [PERSON] et al.