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.