Optimal county-level crop yield prediction using MODIS-based variables and weather data: A comparative study on machine learning models

Abstract Crop yield forecasting depends on many interactive factors including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using extensive datasets of weather, soil, and crop phenology. We propose a convolutional neural network (CNN) which uses the 1-dimentional convolution operation to capture the time dependencies of environmental variables. The proposed CNN, evaluated along with other machine learning models for winter wheat yield prediction in Germany, outperformed all other models tested. To address the seasonality, weekly features were used that explicitly take soil moisture and meteorological events into account. Our results indicated that nonlinear models such as deep learning models and XGboost are more effective in finding the functional relationship between the crop yield and input data compared to linear models and deep neural networks had a higher prediction accuracy than XGboost. One of the main limitations of machine learning models is their black box property. Therefore, we moved beyond prediction and performed feature selection, as it provides key results towards explaining yield prediction (variable importance by time). As such, our study indicates which variables have the most significant effect on winter wheat yield.

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Review of Machine learning models for Crop Yield Prediction

10.4108/eai.7-12-2021.2314568 ◽

2021 ◽

Author(s):

Aravind T

Keyword(s):

Machine Learning ◽

Crop Yield ◽

Yield Prediction ◽

Learning Models ◽

Machine Learning Models

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A Comparative Study of Machine Learning Models for Stock Market Rate Prediction

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i6.985990 ◽

2019 ◽

Vol 7 (6) ◽

pp. 985-990

Author(s):

reeraksha M S ◽

Bhargavi M S

Keyword(s):

Machine Learning ◽

Stock Market ◽

Comparative Study ◽

Learning Models ◽

Rate Prediction ◽

Market Rate ◽

Machine Learning Models

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Prediction of aircraft estimated time of arrival using machine learning methods

The Aeronautical Journal ◽

10.1017/aer.2021.13 ◽

2021 ◽

pp. 1-15

Author(s):

O. Basturk ◽

C. Cetek

Keyword(s):

Machine Learning ◽

Web Application ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Weather Data ◽

Time Of Arrival ◽

Learning Models ◽

Trajectory Data ◽

Different Sources ◽

Machine Learning Models

ABSTRACT In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.

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