## 3 Analysis of Original Research Articles
### Deep Learning  
Deep learning techniques have been extensively applied to forecast agricultural commodity prices, leveraging advanced architectures for improved accuracy, as shown in Table 5. Several studies have integrated neural networks with decomposition and hy \begin{table}
\begin{tabular}{p{28.5 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Refs.** & **Year** & **Commodity Name** & **Dataset** & **Technique** & **Findings** & **Limitation** \\ \hline \multirow{4}{*}{[30]} & \multirow{4}{*}{2022} & & 2004–2013 weather (BARC) and food prices & MLR, AdaBoost, Gradient Boosting, Baggladesh & RFR had R\({}^{2}\) = 0.864; Bagging, Random Forest & Dataset limited to be most correlated to price; ensemble methods & Dataset limited to 2004–2013; only one country analyzed \\ \hline \multirow{4}{*}{[31]} & \multirow{4}{*}{2021} & & Futures corn prices & & & \\  & & & prices & & & \\  & & & (2015–2019), CBOT; daily temperature and precipitation, Refinitiity; supply and demand data, USDA & ML-KNN outperforms ML-RF & MAE 0.0372, RMSE 0.0512, MAPE 0.6908 & More complex algorithms might improve the model \\ \hline \multirow{4}{*}{[32]} & \multirow{4}{*}{2022} & & & & \\  & & & & & \\  & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._bridization techniques to enhance prediction capabilities. For instance, [PERSON] and [PERSON] [33] combined Singular Spectrum Analysis (SSA) with neural networks to forecast commodity futures prices, excelling in trend prediction but facing computational challenges. Similarly, [PERSON] et al. [34] employed a VMD-TDNN hybrid model, which showed superior performance in both level and directional predictions, while [PERSON] and [PERSON] [35] proposed a VMD-SGMD-LSTM model that achieved robust results despite increased computational demands. [PERSON] et al. [36] utilized multivariate standard LSTM with attention mechanisms for Indonesian commodity prices, achieving better results than univariate LSTM, Bi-LSTM, Conv LSTM, and Conv Bi-LSTM. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Refs.** & **Year** & **Commodity Name** & **Dataset** & **Technique** & **Findings** & **Limitation** \\ \hline
[33] & 2018 & Corn, Gold, Crude Oil & Corn, Gold, Crude oil futures CBOT, COMEX, EIA & Corn, Gold, Crude oil futures CBOT, COMEX, EIA & SSA, BPNN, RBFNN, WNN & SSA–NN models outperform baseline NN models & High computational complexity \\ \hline
[34] & 2023 & Corn, Palm Oil, Soybean Oil & Monthly international price for Corn, Palm Oil, Soybean Oil (1960–2021); World Bank & Monthly international price EMD–TDNN, CEEMD–TDNN, O345, RMSE 9.49, DSTAT 90.90 & Directional & Lack of formal methodology to determine the number (n) of extracted modes by VMD \\ \hline
[35] & 2024 & Strong Wheat, Corn, Sugar & Weekly futures prices (2005–2023) from China’s agricultural futures market & Weekly futures & Outperformed benchmark models in 1-step-, 2-step-, and 4-step-ahead & Other price influencing factors could be considered \\ \hline
[36] & 2022 & Rice, Chicken Meat, Eggs, Onions, Garlic, Large Red & Prices of 11 commodities (2017–2021) & & Standard LSTM outperformed Bi-LSTM, Conv LSTM, and Conv Bi-LSTM; & Focused only on a specific set of food commodities \\  & 2022 & Chiles, Cordy Red Chiles, Green Chiles, Cooking Oil, Sugar & Hindenseian & LSTM & Our LSTM, and Conv Bi-LSTM; & Focus on a limited set of food commodities \\ \hline  & & & & & \\  & & & & & \\  & & & & & \\ \end{tabular}
\end{table} 
Table 5: Overview of deep learning techniques in wheat, rice, and corn price prediction.