article · Potato Research
Forecasting potato consumption helps farmers adjust planting volumes, enables sellers to manage inventory levels without wastage, and allows governments to anticipate potential food deficits. This study evaluates multiple machine learning and deep learning models to predict consumption trends up to the year 2030. The evaluated models include stacked long short-term memory, convolutional neural networks, random forest, support vector regressors, k-nearest neighbours, bagging regressors, and dummy regressors. Among these, the stacked long short-term memory model demonstrated superior performance over all other approaches. It attained a coefficient of determination of 98.90 percent, alongside low error metrics including a mean squared error of 0.0081, a mean absolute error of 0.0801, and a median absolute error of 0.0755. These findings demonstrate that deep learning algorithms can provide reliable long-term forecasts of global potato consumption trends.
Accurate food consumption forecasts help prevent market volatility and reduce post-harvest waste. Reliable projections enable agricultural producers to plan crop sizes effectively, assist commercial retailers in maintaining balanced inventories, and allow public policymakers to anticipate supply shortfalls. Ensuring predictable supply chains for staple crops like potatoes directly supports regional and global food security.
The model provides an analytical tool that can be integrated into market intelligence platforms for agricultural producers, commodity traders, and food inventory managers. It is at an applied research stage, having been tested and validated across multiple algorithms to forecast demand to 2030. Commercial deployment would require embedding these forecasting algorithms into enterprise planning software or decision-support platforms used by agribusiness supply chain operators and public food monitoring bodies.
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Abstract Potato consumption forecasting is crucial for several stakeholders in the food market. Due to the market flexibility, the farmers can manipulate the volumes planted for a given type of produce to reduce costs and improve revenue. Consequently, it means that establishing optimal inventories or inventory levels is possible and critical in that sense for the sellers to avoid either inadequate inventory or excessive inventories that may lead to wastage. In addition, governments can predict future food deficits and put measures in place to guarantee that they have a steady supply of food some of the time, especially in regions that involve the use of potatoes. Increased potato-eating anticipation has advantages for the sellers and buyers of the potatoes. The experiments of this study employed various machine learning and deep learning (DL) models that comprise stacked long short-term memory (Stacked LSTM), convolutional neural network (CNN), random forest (RF), support vector regressor (SVR), K -nearest neighbour regressor (KNN), bagging regressor (BR), and dummy regressor (DR). During the study, it was discovered that the Stacked LSTM model had superior performance compared to the other models. The Stacked LSTM model achieved 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 a coefficient of determination ( R 2 ) value of 98.90%. These results demonstrate that our algorithms can reliably forecast global potato consumption until the year 2030.
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DOI: 10.1007/s11540-024-09764-7
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