A Sustainable Approach for Assessing Wheat Production in Pakistan Using Machine Learning Algorithms

As we are advancing deeper into the twenty-first century, new challenges as well as technical opportunities in agriculture are rising. One of these issues is the increasing need for food, which is crucial for supporting the population’s nutritional needs, promoting regional trade, and ensuring food security. Climate change is another ongoing challenge in the shape of changing rainfall patterns, increasing temperatures due to high CO₂ concentrations, and over urbanization which ultimately negatively impact the crop yield. Therefore, for increased food production and the sustainability of agricultural growth, an accurate and timely crop yield prediction could be beneficial. In this paper, artificial intelligence (AI)-based sustainable methods for the evaluation of wheat production (W_P) using multiple linear regression (MLR), support vector machine (SVM), and artificial neural network (ANN) techniques are presented. The historical data of around 60 years, comprising of wheat area (W_A), temperature (T), rainfall (RF), carbon dioxide emissions from liquid and gaseous fusion CE (CE_LF, CE_GF), arable land (AL), credit disbursement (CD), and fertilizer offtake (FO) were used as potential indicators/input parameters to forecast wheat production (W_P). To further support the performance efficiency of computed prediction models, a variety of statistical tests were used, such as R-square (R²), root means square error (RMSE), and mean absolute error (MAE). The results demonstrate that all acceptance standards relating to accuracy are satisfied by the proposed models. However, the SVM outperforms MLR and ANN approaches. Additionally, parametric and sensitivity tests were performed to assess the specific influence of the input parameters.