Prediction of Wheat Production Using Machine Learning Algorithms in northern areas of Pakistan

New avenues of technological opportunities in agriculture are opening as we are further delving deeper into the 21st century, but at the same time, new challenges are emerging. One of these challenges is the growing quantity of food demand, which is highly vital for regional trade, food security, and meeting the nutritious requirements of the population. A timely prediction with accuracy about crop yield could be valuable for greater food production and maintainability of sustainable agricultural growth. This paper presents a predictive model of wheat production using machine learning. The northern areas of Pakistan which grow wheat are selected as a case study due to their importance in the country's agricultural sector. We collected data of five years and selected the best attribute subset related to crop production. We applied twelve (12) algorithms by dividing data samples into three sets. Experimental results helped to shortlist three algorithms for the final analysis i.e. Sequential Minimal Optimization Regression (SMOreg), Multilayer Processing (MLP) and Gaussian Process (GP). The Root Mean Square (RMSE) and Percentage Absolute Difference (PAD) metrics were used to validate the results. The SMOreg obtained the lowest PAD (0.0093) and RMSE (0.5552) values. MLP was a little closer with second-lowest PAD (0.0116) and RMSE (0.737) value. The performance of GP was found lowest due to higher PAD (0.2203) and RMSE (17.7423) values. Our findings confirm the predictive ability of machine learning algorithms on a crop dataset recorded in a localized environment, which could be replicated on other crops and regions.

Automated summary

This study presents a predictive model of wheat production using machine learning, which demonstrates the predictive ability of machine learning algorithms on a crop dataset recorded in a localized environment.