Preharvest Monitoring of Biomass Production

  • Liujun Li
  • Lei Tian
  • Tofael Ahamed


Preharvest monitoring of biomass production is necessary to develop optimized instrumentation and data processing systems for crop growth, health, and stress monitoring and to develop algorithms for field operation scheduling. Some research questions of specific interest are as follows: (1) What are the major crop sensing needs for energy crop health monitoring and productivity improvement? (2) Which sensor/platform should be used for the field data collection? (3) What is the best process for energy crop data-to-knowledge conversion? In this chapter, we first review the basics of remote sensing and its application to energy crops. We then discuss the development of three near-real-time remote sensing systems, namely, a stand-alone tower-based remote sensing system, close proximity data collection vehicle, and an unmanned aerial vehicle-based remote sensing system to monitor crop growth. The physical status of crop growth and biomass accumulation was projected over the growing seasons. The remote sensing systems included multispectral camera, light detection and ranging (LIDAR), and a global position system sensor. The sensing systems were convenient to perform site-specific monitoring of bioenergy crops and collect data in near real time including ground reference information. These nondestructive measurements included bioenergy crop growth monitoring using typical vegetation index and estimation of biomass yield by correlating it with suitable vegetation index. The field experimental data has been presented to correlate with remote sensing data. To understand the crop growth status over the growing season, the remote sensing data could be correlated with ground truth data to develop a model for predicting dry matter biomass.


Normalize Difference Vegetation Index Vegetation Index Unmanned Aerial Vehicle Inertia Measurement Unit Energy Crop 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank Energy Biosciences Institute, University of Illinois at Urbana-Champaign, for supporting the program “Engineering Solutions for Biomass Feedstock Production.”


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Agricultural and Biological EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.Graduate School of Life and Environmental SciencesUniversity of TsukubaTsukubaJapan

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