Open AccessReview of Crop Yield Prediction using Machine Learning Techniques
Author(s) -
Kale Jaydeep Narayan
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.36058
Subject(s) - machine learning , yield (engineering) , artificial neural network , artificial intelligence , computer science , variety (cybernetics) , agriculture , crop , crop yield , crop production , agricultural engineering , agronomy , engineering , ecology , biology , materials science , metallurgy
Machine learning (ML) could be a helpful decision-making tool for predicting crop yields, in addition as for deciding what crops to plant and what to try throughout the crop's growth season. To help agricultural yield prediction studies, variety of machine learning techniques are used. I performed a literature review (LR) to extract and synthesize the algorithms and options employed in crop production prediction analysis. Temperature, rainfall, and soil types are most common measure used in the prediction as per my knowledge, whereas Artificial Neural Networks is the foremost normally used methodology in these models.