Agriculture Reference
In-Depth Information
determine the optimal latent variables (LV). The evaluation index (EI) method was
applied to evaluate the calibration models. As a result, the spectral data of maize
during the period from the yellow-ripe stage to harvest time have increased reflection
at the range of 400 to 700 nm, and decreased reflection in the NIR light range. The
data suggest that the spectral data can reflect changes in the nutrition of maize in the
visible light region and NIR region, even if spectral data are not pretreated. According to
Q
2
value analysis, the optimal number of LV was 4 for moisture, TDN, and OCW, and 3
for CP. High loading weights were found near 680 and 930 nm for moisture, near 680 and
930 nm for TDN, near 680 and 930 nm for CP, near 680 and 930 nm for OCW. The vali-
dation processes of PLSR models obtained a coefficient of determination of measured
and predicted values against moisture TDN, CP, and OCW of maize was about 0.8 or
more. The EI ranked as B for all items shows that the precision is acceptable.
9.5 SUMMARY
Nutrition management and automation is part of the whole agriculture automation system
and is also a complicated system, which includes nutrition information acquisition meth-
ods and instruments, information management methods and instruments, and operation
systems. A new view of the nutrition information acquisition variety was introduced,
which included the individual point, field of view, region, and zone. Based on this sepa-
ration method, we introduced the crop nutrition acquisition methods and instruments,
soil nutrition acquisition methods satellite remote sensing systems, aerial photography
remote sensing system, radar and lidar systems, and nutrition information acquisition
instruments using the agriculture automation system. For the management of nutrition,
we introduced the mainly used sampling methods, nutrition disputation map, and main
automation systems used for nutrition management. For a better understanding of the
nutrition management and automation system, we described two specific application
system samples: one is application system in modern facilities for fruits and vegetables
on agro-ecological information collection and intelligent management, and the other
is application of a crop density sensor for variable rate nitrogen fertilization of winter
wheat. Based on the above nutrition acquisition, management, and operation methods
and instruments, we can build a more complete system of agriculture automation.
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