Abstract

India is an imperial nation, in which agriculture is the primitive profession. Indian economic stability depends on agricultural wealth. Crop yield prediction in agriculture is critical and is chiefly depend upon soil and environment conditions, including rainfall, humidity, and temperature. Ancient farmers were able to decide on the crop to be cultivated, monitor its growth, and determine when it could be harvested. Rapid changes in environmental conditions have made it difficult for the farming community to continue to do so. Thus, agriculture is highly dependent on the new technology for obtaining large profits. Machine learning techniques have taken over the task of prediction in recent times and this work has used several of these to determine crop yield. To ensure that a given machine learning (ML) model works at a high level of precision, it is mandatory to employ efficient feature selection methods to pre-process the raw data into an easily computable Machine Learning friendly dataset. To reduce redundancies and make the ML model more accurate, only data features that have a significant degree of relevance in determining the final output of the model must be employed. Optimal feature selection arises to ensure that only the most relevant features are accepted as a part of the model. The results depict that an ensemble technique offers better prediction accuracy.