For accurate prediction, many studies have been actively conducted to estimate grain crops using machine learning techniques. However, there are only few studies which compare the accuracies using many kinds of machine learning techniques for different types of crop. This study was conducted to estimate corn and soybean yields in Illinois and Iowa in the U.S. through four kinds of machine learning techniques, including deep learning algorithms. ANN (Artificial Neural Network), CNN (Convolutional Neural Network), SSAE (Stacked-Sparse AutoEncoder), and LSTM (Long-Short Term Memory) were used as prediction models, and total 14 years of MODIS (MODerate resolution Imaging Spectroradiometer) data, climatic data and crop yield statistics were used as input variables with the six different periodic scenarios. The accuracies were compared in terms of %RMSE (percentage Root Mean Square Error) and compared to the baseline prediction model, which is DT (Decision tree). As the results, CNN model was most accurate over all with the lowest average %RMSE errors in corn (9.36%) and SSAE model in soybean (10.05%) respectively. The best periodic scenario for corn yield prediction was May to September, and for soybean was June to August. This study identified suitable scenario and prediction technique for corn and soybean yield, which will be useful for farming activities and agricultural planning.
|Publication status||Published - 2020|
|Event||40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019 - Daejeon, Korea, Republic of|
Duration: 2019 Oct 14 → 2019 Oct 18
|Conference||40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019|
|Country/Territory||Korea, Republic of|
|Period||19/10/14 → 19/10/18|
Bibliographical noteFunding Information:
This work was carried out with the support of ?Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ009978)? Rural Development Administration, Republic of Korea.
© 2020 40th Asian Conference on Remote Sensing, ACRS 2019: "Progress of Remote Sensing Technology for Smart Future". All rights reserved.
All Science Journal Classification (ASJC) codes
- Information Systems