Login
Toggle navigation
Home
ICPA
Conference
Abstract Management
Abstract Topic Groups
Author Instructions
Registration
Registration Information
16th ICPA - Conference Registration
Registrants Map
Hotel and Travel Information
Tour
Workshops
Exhibit Hall Map
Sponsors
Conference Program
General Outline
Oral Program
Poster Program
Student Poster Awards
Keynote
Plenary Session
Awards
Photos
Conference Survey
Proceedings
Leadership
ISPA Leadership
Officers
Past Presidents
Officer Responsibilities
Country Representatives
Communities
Community Guidance
On-Farm Experimentation
Nitrogen
Latin America
Economics
African Association for Precision Agriculture
Membership
ISPA Member Benefits
Membership Form
Events
ISPA Events
ACPA
ACPA Proceedings
AfCPA
AfCPA Proceedings
CLAP
CLAP Proceedings
ECPA
ECPA Proceedings
ICPA
ISPA Webinars
OFE
AAPA
Latin American
Robotics and Automation Symposium
Event Overview
Registration
Program
Venue
Speakers
About ISPA
Newsletters
History
Jobs
Precision Ag Definition
Agriculture Course Database Submission
Publications
ICPA Proceedings
ECPA Proceedings
Contact Us
Members
Suggestion Form
Conference
Abstract Management
Abstract Topic Groups
Author Instructions
Registration
Registration Information
16th ICPA - Conference Registration
Registrants Map
Hotel and Travel Information
Tour
Workshops
Exhibit Hall Map
Sponsors
Conference Program
General Outline
Oral Program
Poster Program
Student Poster Awards
Keynote
Plenary Session
Awards
Photos
Conference Survey
Proceedings
Proceedings
Search
Authors
Topics
Years
Types
Find matching any:
Reset
» Add more types
Add filter to result:
Near-Real-Time Remote Sensing And Yield Monitoring Of Biomass Crops
1
Y. Zhao,
2
L. Li,
2
K. C. Ting,
2
L. F. Tian,
3
T. Ahamed
1. Northeast Agricultural University
2. University of Illinois
3. University of Tsukuba
The demand for bioenergy crops production has increased tremendously by the biofuel industry for substitution of traditional fuels due to the economic availability and environmental benefits. Pre-Harvest 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. To cope with the problems of missing critical timing of field crop conditions in the traditional remote sensing process (e.g., satellite or aerial imaging systems), an experimental near-real-time remote sensing platform with high spectral resolution, spatial resolution and temporal resolution was proposed, designed and developed for a biomass production field pre-harvesting crop monitoring. The crop monitoring system can scan the crop field within 15 minutes. 91 images are captured daily to cover a 35-acre crop field. The multi-spectral imaginary of a bioenergy crop with spatial resolution of 100 mm/pixel was automatically collected and an intelligent control algorithm, e.g., camera movement such as zoom, focus and robust real-time multi-spectral camera parameters adjustment such as gain and exposure time under varying natural lighting conditions in the field, were developed to automatically capture high quality daily images through the growing season. The Normalized Difference Vegetation Index (NDVI) was calculated for understanding of the temporal vegetation response variations in the crop growth cycle over the growing seasons after imaginary geometrical corrections and geo-referencing. Special algorithms were developed to compile the high-resolution images to form an entire field crop index map. The image processing results from the proposed near-real-time remote sensing system were then compared to the biomass yield data in this paper. With the crop index map as a field condition-monitoring tool, the accurately geo-referenced biomass yield data points (1m x 1m) were generated through manually harvested, dried and weighed Miscanthus plants during the growing season.
A novel “daily canopy reflectance accumulation” algorithm was developed to match the field yield data. To increase data processing accuracy, both GPS readings and (manually identified) image patterns were used to locate the manually harvested data points. Preliminary analysis results show that the crop monitoring system can generate a high-resolution yield map that can explain 84.96% of the daily (distributed) biomass gain during the growing season. Moreover, the in-field growth variation and the plant growth pattern of the Miscanthus were derived and recognized. The biomass yield was predicted during an earlier growing season and provided decision support for the optimum harvest scheduling and site-specific crop management for the biofuel industry.
Keyword
: Remote sensing, Precision agriculture (PA), Site-specific crop management (SSCM), multi-spectral imaging, growth condition monitoring, Normalized Difference Vegetation Index (NDVI), ground reference data, yield prediction, harvest schedule
Y. Zhao
L. Li
K. C. Ting
L. F. Tian
T. Ahamed
Remote Sensing Applications in Precision Agriculture
Oral
2014
Download paper