Estimating Crop Yields Using Machine Learning Techniques

Abstract

India's economy heavily relies on agriculture, with a significant portion of the population either directly involved in farming or trading the end products. Agricultural production of food grains in India is vast and has a tremendous impact on the nation's economy. Farmers are eager to know the yield they can expect, and this study attempts to quantify the technological factors that affect yields. By analysing factors such as season, area, temperature, humidity, moisture, soil type, crop type, and nutrient levels, the study aims to predict future crop yields accurately. Additionally, the model uses data on crop production in different districts and years to train and test various machine learning (ML) Techniques like Decision tree, Linear regression, K-Nearest Neighbors along with a deep learning architecture. The above listed models are compared based on different performance metrics such as RMSE and MAE etc. This study's results can assist agronomists a way to increase their incomes along with country economy by crop yield prediction with a fertilizer need to be used for that given area.