1st Workshop BOOK - project RHEA

2. Step 1: Field inspection
Robotics and associated High technologies and Equipment for Agriculture
Information on weed spatial distribution and abundance will be generated from
aerial images obtained at different times prior to herbicide spraying and from
information obtained from ground vehicles at spraying time. Aerial inspection of
the fields in spring, when some broadleaved weeds are in full bloom (and can be
easily discriminated from the crop) provide an excellent opportunity to
discriminate and map infestations of selected weed species. Numerous studies
have shown that detection of late-season weed infestation has considerable
possibilities when the weeds exceed the crop canopy and the spectral differences
between crops and weeds are maximum (López-Granados, 2011). Detection of
bright coloured red poppies (Papaver rhoeas) on the green background provided by
the crop may be a paradigmatic example of this situation. Monitoring weed
populations at various times will be achieved using unmanned aerial vehicles
(UAV). These vehicles provide high spectral, spatial and temporal resolutions with a
relatively low cost (López-Granados, 2011). Additional georeferenced data on weed
infestations will be obtained at spraying time by the cameras or sensors located on
the sprayers (Andújar et al., 2011; Burgos-Artizzu et al., 2011).
3. Step 2: Long-term decisions
Farmers usually consider two different time-scales for decision making: long term
(with a 3-5 year horizon) and current year. Consequently, a comprehensive system
should include a long-term planning tool allowing users to consider various cultural
practices (rotations, tillage systems) conducted over several years and a within
season planning tool to investigate a range of weed control options in a single
season (Parsons et al., 2009).
The objective of the our long-term decision module is to optimize the choice of
crop and herbicide rotations as well as the tillage system throughout a rotation (up
to 5 years) defined by the user, trying to find the best long-term strategy. The
decision process could be made using a finite horizon stochastic dynamic program,
trying to identify the policy that maximizes the long term expected discounted
return. Other option could be using a set of decision rules coupled with a rule
editor and an inference engine. In order to take these decisions, different types of
data are required.
Historic maps, constructed using the weed distribution data obtained in the
previous step, provide one major element for this process. In addition, empirical
knowledge of the farmer, gathered through many years of working the field, is also
a valuable data source that should be exploited, particularly when digital maps with
historical records are not available. All this information is stored in a `Weed spatial´
database.
Biological data on the major weeds that are present in the field are required to
assess the actual risks posed by those plants and the opportunities and possibilities
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