Agriculture Reference
In-Depth Information
There were two shortcomings with the WMTS as originally devised. First, users
must interact with the system, informing it when a harvester or boll buggy is dumping
a basket of cotton. This interaction causes the attention of the operator to be diverted
from the task at hand and results in reduced reliability, efficiency, and safety. Second,
the system is not capable of accepting multiple machines of the same type such as
more than one harvester. On large farming operations, producers often use multiple
harvesters, boll buggies, and module builders in the same large field. To address
these two issues, new features were added to the system to make it capable of auto-
matic wireless message triggering when the harvester or boll buggy was dumping a
basket, and compatible with multiple instances of similar machinery (i.e., more than
one harvester, boll buggy, and/or module builder) in a given field (Sjolander et al.,
2011a, 2011b). Automatic wireless message triggering was effected through (1) using
an inclinometer to sense the tilt angle of the harvester or boll-buggy basket to deter-
mine when a dump was taking place, (2) using load cells to sense the remaining
load in the basket to verify the completeness of a basket dump, and (3) using RFID
(radio-frequency identification) to identify machines involved in a load transfer so
that wireless messages could be sent to specific machines when multiple machines
were present. The improved WMTS was successfully field tested, and results indi-
cated that the automated WMTS worked as designed. With the improved WMTS,
module averages of fiber-quality data can be mapped to their original locations on
the producer's field based on automatically generated spatial data. Once these maps,
along with maps of yield and cost, have been produced for a given field, an accurate
module-level profit map can be created.
The WMTS effectively maps cotton fiber quality, but it is accurate only to the
cotton-module level. To make a fiber quality map with higher resolution, a sensor
is required to measure cotton fiber quality in real time as cotton is harvested in the
field. Toward this goal, a prototype cotton fiber quality sensor was developed based
on the characteristics of the cotton fiber reflectance spectrum. The sensor consists of
a VisGaAs camera, optical bandpass filters, a halogen light source, and an image col-
lection and processing system. Images of lint samples in three near-infrared (NIR)
wavebands (1450, 1550, and 1600 nm) were acquired and analyzed to determine the
relationship between histogram-based image pixel values and cotton fiber micro-
naire. Results showed that the sensor was capable of accurately estimating the fiber
micronaire:
R
2
= 0.99 (Sui et al., 2008). A ruggedized prototype of the multispectral
fiber quality sensor was developed for installation on a cotton harvester. A filter
wheel was added to the sensor system, and software was used to control the selec-
tion of optical filters so that images at selected wavebands could be acquired auto-
matically. The ruggedized sensor acquires images of seed cotton, which contains
a considerable amount of foreign matter, at three NIR wavebands and one visible
band, used to exclude pixels that represent foreign matter before determining fiber
quality with the NIR images. Results again showed a close relationship between NIR
reflectivity of seed cotton and the fiber micronaire values (Schielack et al., 2009).
This sensor prototype could be adapted for measuring cotton fiber quality along
with spatial data from a GPS receiver as the cotton is harvested in the field, making
it possible to generate cotton fiber quality maps. The sensor also has the potential to
be used for segregating cotton at harvest based on fiber quality.
