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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Forage and Livestock Production Research » Research » Publications at this Location » Publication #337991

Research Project: Integrated Forage Systems for Food and Energy Production in the Southern Great Plains

Location: Forage and Livestock Production Research

Title: Application of statistical machine learning algorithms in precision agriculture

Author
item Sridharan, Mohan - University Of Auckland
item Gowda, Prasanna

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 2/7/2017
Publication Date: 10/16/2017
Citation: Sridharan, M., Gowda, P.H. 2017. Application of statistical machine learning algorithms in precision agriculture. In: International Conference on Precision Agriculture Abstracts & Proceedings, October 16-18, 2017, Hamilton, New Zealand. p. 1-6.

Interpretive Summary: Abstract only.

Technical Abstract: Remote sensing can facilitate rapid collection of data in agriculture at relatively low cost. Advancements in unmanned aerial vehicles and sensor technology, along with a significant reduction in the cost of acquiring data, have enabled us to collect and process remote sensing data in real time. Such an approach is widely used in precision agriculture for estimating crop and soil characteristics such as leaf area index, biomass, crop stress, evapotranspiration, crop yield, and soil organic matter. Remote sensing techniques typically use predictive models (e.g., linear, quadratic, power and exponential regression) that are based on ordinary least square (OLS) regression. However, the performance of these predictive models deteriorates when the effects of sun-surface sensor geometry, background reflectance and atmosphere-induced variations on spectral reflectance or spectral vegetation indices are larger than the variations in the crop or soil characteristics of interest. Any errors in the predicted soil and crop characteristics may, in turn, adversely affect farm inputs and outputs and thus the net profits. In recent years, machine learning algorithms such as artificial neural networks, support vector machines and Gaussian Processes are being explored for developing predictive models for agricultural applications. Models based on such machine learning algorithms are known to provide substantial benefits over OLS models. In this paper, we theoretically and experimentally compare and contrast the accuracy of OLS and statistical machine learning models for estimating leaf area index, crop yield, gross primary productivity and crop water use (or evapotranspiration). Furthermore, we discuss the significant benefits of the widespread use of statistical machine learning algorithms in precision agriculture.