controlled according to natural irradiance was allowed to vary more than usual.
When light conditions were no appropriate for crop development, heating energy
was reduced. On the other hand, when irradiance rises, plants are able to utilize
higher temperatures and CO 2 concentrations more efficiently. Applying this TI
modified techniques; energy saving per season between 20 and 38 % were
achieved.
MPC is an advanced control technique commonly used for industrial application but recently applied in the field of protected agriculture. The objective is
predicting the greenhouse variables behavior. A contribution to this scheme was
offered by Van Straten et al. (2000), where information about crop growth simplifies the design of greenhouse control strategies to obtain a truly economical
control strategy. This approach leads to the concept of selecting processes by time
response where the short-term effects like photosynthesis and evapotranspiration
are dealt with by an automated model predictive optimal controller, while the
long-term effects are left to the grower. Energy savings are also achieving with this
methodology, but in comparison with TI which offers flexible set points, the
predictions of MPC allow the use of a soft optimal control effort (Piñón et al.
2005). Another important characteristic of MPC is its multivariable constraint
handling capacity and the ability to set cost for each prediction. These characteristics were exploited including restrictions to energy and water consumption.
For example, error and control signals are weighted differently during the day and
the night, the errors weights are much higher during the day that at night and the
constrains are relaxed during the night (Blasco et al. 2007).
As was previously mentioned, the grower intervention had not been completely
avoided in the crop management. Then, decision support tools that assists the grower
to choose the most appropriate climate regimen was proposed (Gupta et al. 2010).
These regimens choose the most appropriate climate for plants according with its
phonologic state in order to obtain the optimal gains of sustainability and plant
quality. The greenhouse climate and crop model are studied separately and jointly
considering the effects of six different regimes with increasing degrees of freedom
for various climate variables which include: crop model, temperature integration,
dynamic humidity control, and negative DIF regimes (DIF ¼ the difference between
average day temperature and average night temperature, and therefore reduces the
use of chemical growth regulators) (Körner and Van Straten 2008).
Phytocontrol is the new theory which proposes the use of the plant physiological responses as input signal to establish the set points in the climate controller
(Ton et al. 2001). Also, this has not proved to be a stable and reliable method,
because it is necessary to gather a lot of information to prove the reliability of this
theory (Linker and Seginer 2003). Nevertheless, different types of controllers have
emerged demonstrating advantages and disadvantages between them, better performance for some actions among other characteristics.
13 Instrumentation and Control to Improve the Crop Yield
393
When light conditions were no appropriate for crop development, heating energy
was reduced. On the other hand, when irradiance rises, plants are able to utilize
higher temperatures and CO 2 concentrations more efficiently. Applying this TI
modified techniques; energy saving per season between 20 and 38 % were
achieved.
MPC is an advanced control technique commonly used for industrial application but recently applied in the field of protected agriculture. The objective is
predicting the greenhouse variables behavior. A contribution to this scheme was
offered by Van Straten et al. (2000), where information about crop growth simplifies the design of greenhouse control strategies to obtain a truly economical
control strategy. This approach leads to the concept of selecting processes by time
response where the short-term effects like photosynthesis and evapotranspiration
are dealt with by an automated model predictive optimal controller, while the
long-term effects are left to the grower. Energy savings are also achieving with this
methodology, but in comparison with TI which offers flexible set points, the
predictions of MPC allow the use of a soft optimal control effort (Piñón et al.
2005). Another important characteristic of MPC is its multivariable constraint
handling capacity and the ability to set cost for each prediction. These characteristics were exploited including restrictions to energy and water consumption.
For example, error and control signals are weighted differently during the day and
the night, the errors weights are much higher during the day that at night and the
constrains are relaxed during the night (Blasco et al. 2007).
As was previously mentioned, the grower intervention had not been completely
avoided in the crop management. Then, decision support tools that assists the grower
to choose the most appropriate climate regimen was proposed (Gupta et al. 2010).
These regimens choose the most appropriate climate for plants according with its
phonologic state in order to obtain the optimal gains of sustainability and plant
quality. The greenhouse climate and crop model are studied separately and jointly
considering the effects of six different regimes with increasing degrees of freedom
for various climate variables which include: crop model, temperature integration,
dynamic humidity control, and negative DIF regimes (DIF ¼ the difference between
average day temperature and average night temperature, and therefore reduces the
use of chemical growth regulators) (Körner and Van Straten 2008).
Phytocontrol is the new theory which proposes the use of the plant physiological responses as input signal to establish the set points in the climate controller
(Ton et al. 2001). Also, this has not proved to be a stable and reliable method,
because it is necessary to gather a lot of information to prove the reliability of this
theory (Linker and Seginer 2003). Nevertheless, different types of controllers have
emerged demonstrating advantages and disadvantages between them, better performance for some actions among other characteristics.
13 Instrumentation and Control to Improve the Crop Yield
393