The study "Potato Consumption Forecasting Based on a Hybrid Stacked Deep Learning Model" by Marwa Eed et al. explores the importance of forecasting potato consumption for various stakeholders in the food market. The research aims to optimize inventory levels and predict future food deficits, particularly in regions where potatoes are a significant crop. The study employs several machine learning and deep learning models, including Stacked LSTM, CNN, RF, SVR, KNN, BR, and DR. The Stacked LSTM model outperformed the others, achieving a mean squared error (MSE) of 0.0081, a mean absolute error (MAE) of 0.0801, a median absolute error (MedAE) of 0.0755, and an $R^2$ value of 98.90%. These results indicate that the algorithms can reliably forecast global potato consumption until 2030. The introduction highlights the significance of potatoes as a globally consumed crop, ranking third in annual production at 375 million tons. Potatoes are a rich source of nutrients, including carbohydrates, dietary fiber, vitamins, and minerals, and are known for their low environmental impact. The consumption patterns vary globally, with Eastern Europe leading in per capita consumption, while some African countries have lower rates. Cultural and environmental factors influence these variations. The study also discusses the role of artificial intelligence (AI) in agriculture, emphasizing how AI tools can improve resource management and reduce waste, thereby enhancing agricultural productivity.The study "Potato Consumption Forecasting Based on a Hybrid Stacked Deep Learning Model" by Marwa Eed et al. explores the importance of forecasting potato consumption for various stakeholders in the food market. The research aims to optimize inventory levels and predict future food deficits, particularly in regions where potatoes are a significant crop. The study employs several machine learning and deep learning models, including Stacked LSTM, CNN, RF, SVR, KNN, BR, and DR. The Stacked LSTM model outperformed the others, achieving a mean squared error (MSE) of 0.0081, a mean absolute error (MAE) of 0.0801, a median absolute error (MedAE) of 0.0755, and an $R^2$ value of 98.90%. These results indicate that the algorithms can reliably forecast global potato consumption until 2030. The introduction highlights the significance of potatoes as a globally consumed crop, ranking third in annual production at 375 million tons. Potatoes are a rich source of nutrients, including carbohydrates, dietary fiber, vitamins, and minerals, and are known for their low environmental impact. The consumption patterns vary globally, with Eastern Europe leading in per capita consumption, while some African countries have lower rates. Cultural and environmental factors influence these variations. The study also discusses the role of artificial intelligence (AI) in agriculture, emphasizing how AI tools can improve resource management and reduce waste, thereby enhancing agricultural productivity.