These are recent papers concerning the state-of-the-art (SOTA) in precision agriculture and using artificial intelligence (AI) and machine learning (ML) in the agricultural domain. Also listed here are works that analyze the quality of various models in this domain, comparing statistical, ML, and deep learning (DL) models and their data considerations:

• Random Forests for Global and Regional Crop Yield Predictions by Jig Han Jeong et. al - Shows ML models, especially random forest, to be promising in predicting crop yields when correlated with weather data, motivating us to to experiment with such models on alfalfa biomass yields, which they do not consider
• A Scalable Machine Learning System for Pre-Season Agriculture Yield Forecast by Igor Oliveira et. al - Proposes a technique to forecast future crop yields as opposed to using data from the current season to predict current yields, motivating us to explore similar time-series based methods; however, they rely on various satellite data for predicting crops other than alfalfa
• Significant remote sensing vegetation indices: A review of developments and applications by J. Xue et. al - Shows ML to be promising in predicting crop yields when using remote sensors to collect data and help calculate vegetation indices, motivating us to try similar models but with simpler weather-based data
• Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran by S Ayoubi et. al - Shows artificial neural networks to be promising in helping predict barley yields based on data describing soil properties
• Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review by A Chlingaryan et. al - Shows ML to be useful in predicting crops, helping motivate our research
• Predictive ability of machine learning methods for massive crop yield prediction by A González Sánchez et. al - Shows ML to be suitable for helping with agricultural planning on a massive scale
• An assessment of pre-and within-season remotely sensed variables for forecasting corn and soybean yields in the United States by David M. Johnson - Shows ML to be useful, but relies on data from complex remote sensor technology
• Forecast Evaluation with Stata by Robert Alan Yaffee - Proposes Stata as a tool for use in evaluating ML forecasting models, which I am currently exploring
• An Empirical Comparison of Machine Learning Models for Time Series Forecasting by Nesreen K. Ahmed et. al