Towards crop yield estimation at a finer spatial resolution using machine learning methods over agricultural regions

2021 ◽  
Author(s):  
Dehai Liao ◽  
Jun Niu ◽  
Na Lu ◽  
Qianxi Shen
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Spatial Resolution ◽  
Yield Estimation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Agricultural Regions

Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods

2016 ◽  
Vol 218-219 ◽  
pp. 74-84 ◽  
Author(s):  
Michael D. Johnson ◽  
William W. Hsieh ◽  
Alex J. Cannon ◽  
Andrew Davidson ◽  
Frédéric Bédard
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Vegetation Indices ◽  
Remotely Sensed ◽  
Learning Methods ◽  
Canadian Prairies ◽  
Machine Learning Methods ◽  
Yield Forecasting ◽  
Crop Yield Forecasting

scholarly journals Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space

2019 ◽  
Vol 11 (3) ◽  
pp. 266 ◽  
Author(s):  
Giles Foody ◽  
Feng Ling ◽  
Doreen Boyd ◽  
Xiaodong Li ◽  
Jessica Wardlaw
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Spatial Resolution ◽  
Earth Observation ◽  
Sustainable Development Goal ◽  
Learning Methods ◽  
Development Goal ◽  
Machine Learning Methods ◽  
Brick Kilns ◽  
Fine Spatial Resolution

A large proportion of the workforce in the brick kilns of the Brick Belt of Asia are modern-day slaves. Work to liberate slaves and contribute to UN Sustainable Development Goal 8.7 would benefit from maps showing the location of brick kilns. Previous work has shown that brick kilns can be accurately identified and located visually from fine spatial resolution remote-sensing images. Furthermore, via crowdsourcing, it would be possible to map very large areas. However, concerns over the ability to maintain a motivated crowd to allow accurate mapping over time together with the development of advanced machine learning methods suggest considerable potential for rapid, accurate and repeatable automated mapping of brick kilns. This potential is explored here using fine spatial resolution images of a region of Rajasthan, India. A contemporary deep-learning classifier founded on region-based convolution neural networks (R-CNN), the Faster R-CNN, was trained to classify brick kilns. This approach mapped all of the brick kilns within the study area correctly, with a producer’s accuracy of 100%, but at the cost of substantial over-estimation of kiln numbers. Applying a second classifier to the outputs substantially reduced the over-estimation. This second classifier could be visual classification, which, as it focused on a relatively small number of sites, should be feasible to acquire, or an additional automated classifier. The result of applying a CNN classifier to the outputs of the original classification was a map with an overall accuracy of 94.94% with both low omission and commission error that should help direct anti-slavery activity on the ground. These results indicate that contemporary Earth observation resources and machine learning methods may be successfully applied to help address slavery from space.

scholarly journals Modify the Accuracy of MODIS PWV in China: A Performance Comparison Using Random Forest, Generalized Regression Neural Network and Back−Propagation Neural Network

2021 ◽  
Vol 13 (11) ◽  
pp. 2215
Author(s):  
Zhaohui Xiong ◽  
Xiaogong Sun ◽  
Jizhang Sang ◽  
Xiaomin Wei
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Water Vapor ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Generalized Regression Neural Network ◽  
Learning Methods ◽  
Machine Learning Methods

Water vapor plays an important role in climate change and water cycling, but there are few water vapor products with both high spatial resolution and high accuracy that effectively monitor the change of water vapor. The high precision Global Navigation Satellite System (GNSS) Precipitable Water Vapor (PWV) is often used to calibrate the high spatial resolution Moderate−resolution Imaging Spectroradiometer (MODIS) PWV to produce new PWV product with high accuracy and high spatial resolution. In addition, the machine learning method has a good performance in modifying the accuracy of MODIS PWV. However, the accuracy improvement of different machine learning methods and different modeling timescale is different. In this article, we use three machine learning methods, namely, the Random Forest (RF), Generalized Regression Neural Network (GRNN), and Back−propagation Neural Network (BPNN) to calibrate MODIS PWV in 2019, at annual and monthly timescales. We also use the Multiple Linear Regression (MLR) method for comparison. The root mean squares (RMSs) at the annual timescale with the three machine learning methods are 4.1 mm (BPNN), 3.3 mm (RF), and 3.9 mm (GRNN), and the average RMSs become 2.9 mm (BPNN), 2.8 mm (RF), and 2.5 mm (GRNN) at the monthly timescale. Those results are all better than the MLR method (5.0 mm at the annual timescale and 4.6 mm at the monthly timescale). When there is an obvious variation pattern in the training sample, the RF method can capture the pattern to achieve the best results since the RF achieves the best performance at the annual timescale. Dividing such samples into several sub−samples each having higher internal consistency could further improve the performance of machine learning methods, especially for the GRNN, since GRNN achieves the best performance at the monthly timescale, and the performance of those three machine learning methods at the monthly timescale is better than that of annual timescale. The spatial and temporal variation patterns of the RMS values are significantly weakened after the modeling by machine learning methods for both three methods.

scholarly journals Predictive ability of machine learning methods for massive crop yield prediction

2014 ◽  
Vol 12 (2) ◽  
pp. 313 ◽  
Author(s):  
Alberto Gonzalez-Sanchez ◽  
Juan Frausto-Solis ◽  
Waldo Ojeda-Bustamante
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Predictive Ability ◽  
Yield Prediction ◽  
Learning Methods ◽  
Machine Learning Methods

Application of machine learning methods for automatic interpretation of open hole logging data

2020 ◽  
pp. 44-47
Author(s):  
M.A. Basyrov ◽  
◽  
A.V. Akinshin ◽  
I.R. Makhmutov ◽  
Yu.D. Kantemirov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Automatic Interpretation ◽  
Open Hole
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