Next Article in Journal
The Ouroboros Model, Proposal for Self-Organizing General Cognition Substantiated
Previous Article in Journal
Remaining Useful Life Prediction Using Temporal Convolution with Attention
Previous Article in Special Issue
Testing the Suitability of Automated Machine Learning for Weeds Identification
Article

Using Machine Learning and Feature Selection for Alfalfa Yield Prediction

1
Institute for Artificial Intelligence, University of Georgia, 515 Boyd Graduate Studies, 200 D. W. Brooks Drive, Athens, GA 30602, USA
2
Department of Computer Science, University of Georgia, 415 Boyd Graduate Studies, 200 D. W. Brooks Drive, Athens, GA 30602, USA
3
Department of Crop and Soil Sciences, Institute of Plant Breeding Genetics and Genomics, University of Georgia, 4317 Miller Plant Science, Athens, GA 30602, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Ioannis Ampatzidis, Spyros Fountas, Wonsuk (Daniel) Lee and Panos M. Pardalos
AI 2021, 2(1), 71-88; https://doi.org/10.3390/ai2010006
Received: 30 December 2020 / Revised: 1 February 2021 / Accepted: 1 February 2021 / Published: 14 February 2021
(This article belongs to the Special Issue Artificial Intelligence in Agriculture)
Predicting alfalfa biomass and crop yield for livestock feed is important to the daily lives of virtually everyone, and many features of data from this domain combined with corresponding weather data can be used to train machine learning models for yield prediction. In this work, we used yield data of different alfalfa varieties from multiple years in Kentucky and Georgia, and we compared the impact of different feature selection methods on machine learning (ML) models trained to predict alfalfa yield. Linear regression, regression trees, support vector machines, neural networks, Bayesian regression, and nearest neighbors were all developed with cross validation. The features used included weather data, historical yield data, and the sown date. The feature selection methods that were compared included a correlation-based method, the ReliefF method, and a wrapper method. We found that the best method was the correlation-based method, and the feature set it found consisted of the Julian day of the harvest, the number of days between the sown and harvest dates, cumulative solar radiation since the previous harvest, and cumulative rainfall since the previous harvest. Using these features, the k-nearest neighbor and random forest methods achieved an average R value over 0.95, and average mean absolute error less than 200 lbs./acre. Our top R2 of 0.90 beats a previous work’s best R2 of 0.87. Our primary contribution is the demonstration that ML, with feature selection, shows promise in predicting crop yields even on simple datasets with a handful of features, and that reporting accuracies in R and R2 offers an intuitive way to compare results among various crops. View Full-Text
Keywords: alfalfa; cross validation; feature selection; machine learning; regression; yield prediction alfalfa; cross validation; feature selection; machine learning; regression; yield prediction
Show Figures

Figure 1

MDPI and ACS Style

Whitmire, C.D.; Vance, J.M.; Rasheed, H.K.; Missaoui, A.; Rasheed, K.M.; Maier, F.W. Using Machine Learning and Feature Selection for Alfalfa Yield Prediction. AI 2021, 2, 71-88. https://doi.org/10.3390/ai2010006

AMA Style

Whitmire CD, Vance JM, Rasheed HK, Missaoui A, Rasheed KM, Maier FW. Using Machine Learning and Feature Selection for Alfalfa Yield Prediction. AI. 2021; 2(1):71-88. https://doi.org/10.3390/ai2010006

Chicago/Turabian Style

Whitmire, Christopher D., Jonathan M. Vance, Hend K. Rasheed, Ali Missaoui, Khaled M. Rasheed, and Frederick W. Maier. 2021. "Using Machine Learning and Feature Selection for Alfalfa Yield Prediction" AI 2, no. 1: 71-88. https://doi.org/10.3390/ai2010006

Find Other Styles

Article Access Map by Country/Region

1
Back to TopTop