An Application of K-Nearest-Neighbor Regression in Maize Yield Prediction

Predictive analytics utilizes historical data and knowledge to predict future outcomes and provides a method for evaluating the accuracy and reliability of these forecasts. Artificial Intelligence is a tool of predictive analytics.  AI trains computers to learn human behaviors like learning, judgment, and decision-making while simulating intelligent human behavior using computers and has received a lot of attention in almost all areas of research. Machine learning is a branch of Artificial Intelligence that has been used to solve classification and regression problems. Machine learning advancements have aided in boosting agricultural gains. Yield prediction is one of the agricultural sectors that has embraced machine learning. K Nearest Neighbor (KNN) Regression is a regression algorithm used in machine learning for prediction tasks. KNN Regression is like KNN Classification, except that KNN Regression predicts a constant output value for a given input instead of predicting a class label. The basic idea behind KNN Regression is to find the K nearest neighbors to a given input data point based on a distance metric and then use the average (or weighted average) of the output values of these K neighbors as the predicted output for the input data point. The distance metric used in KNN Regression can vary depending on the data type being analyzed, but common distance metrics include Euclidean distance, Manhattan distance, and Minkowski distance. This paper presents the application of KNN regression in maize yield prediction in Uasin Gishu county, in north rift region of Kenya. Questionnaires were distributed to 900 randomly selected maize farmers across the thirty wards to obtain primary data. With a Train Test split ration of 80:20, KNN regression algorithm was able to predict maize yield and its prediction performance was evaluated using Root Mean Squared error-RMSE=0.4948, Mean Squared Error-MSE =0.2803, Mean Absolute Error-MAE = 0.4591 and Mean Absolute Percentage Error-MAPE = 36.17. According to the study findings, the algorithm was able to predict maize yield in the maize producing county.