## 2 Research Methodology
### Search Strategy  
The Following search string has been used to find relevant articles to conduct this study.  
TITLE-ABS-KEY ((\"machine learning\" OR \"deep learning\" OR \"reinforcement learning\" OR \"neural networks\" OR \"random forests\" OR \"support vector machines\" OR \"support vector regression\" OR \"SVR\" OR \"LSTM\" OR \"Bayesian networks\" OR \"hybrid models\" OR \"ensemble techniques\" OR \"gradient boosting\" OR \"AutoML\" OR \"extreme learning machine\" OR \"ELM\" OR \"decision trees\" OR \"KNN\" OR \"k-nearest neighbors\" OR \"principal component analysis\" OR \"PCA\" OR \"recurrent neural networks\" OR \"RNN\")  
AND  
(\"wheat\" OR \"maize\" OR \"corn\" OR \"rice\" OR \"paddy\")  
AND  
(\"price prediction\" OR \"price forecasting\" OR \"future price\" OR \"price trends\" OR \"market forecasting\" OR \"commodity price forecasting\" OR \"price volatility\" OR \"price dynamics\" OR \"real-time price forecasting\" OR \"long-term price forecasting\" OR \"short-term price forecasting\"))  
In the query used above, SVR is Support Vector Regression, LSTM is Long Short-Term Memory, RNN is Recurrent Neural Network, RF is Random Forest, KNN is k-Nearest Neighbors, and VAR is Vector Autoregression.  
The search for original research articles in the field of agriculture commodity price prediction involved collecting articles from Scopus.  
The Scopus database was selected for the comprehensive and consistent indexing of peer-reviewed journals in computer science, engineering, and agricultural sciences. This choice supported the reproducibility and transparency of the review and ensured that high-quality original research articles were included. The search was conducted in January 2025 and included studies published up to 31 December 2024. No additional databases, registries, websites, or gray literature were consulted.  
The choice to use a single database may limit the breadth of the literature covered; we acknowledge this limitation and suggest that future reviews expand their scope to include other databases, such as Web of Science and Google Scholar. Similar approaches have been adopted in other recent SLRs (e.g., [PERSON] [13]; [PERSON] and [PERSON] [14]; [PERSON] and [PERSON] [15]; [PERSON] et al. [16]).  
A total of 75 articles were retrieved initially.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Objective (RO)** & **Research Question (RQ)** & **Motivation** \\ \hline RO2: To investigate the challenges and limitations encountered in the implementation of these forecasting models. & RQ2: What are the key challenges and limitations reported in the literature regarding data quality, model complexity, interpretability, and computational demands? & To highlight the obstacles and limitations faced by researchers and practitioners in developing accurate and reliable forecasting models. \\ \hline RO3: To focus the review on staple crops that are central to global food security. & RQ3: Why are wheat, corn, and rice chosen as the target commodities for this review? & To justify the scope of the study based on the essential role of these crops in global diets and markets. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._The following filters were applied:  
* Language: English
* Document type: Journal articles only  
These filters ensured that the review focused on high-quality, peer-reviewed studies published in English.