292
G. K. Shankhdhar et al.
Fig. 12 Generic crop knowledge module
7 Scenario
A “virtual discussion room” in ABSMSA as pointed out earlier is shown with an
example scenario using smart services is considered below:
• The satellite identifies that one of the farms of the horticulturist is abnormal triggered by Supervisor Agent, having a new in-homogeneity by reading the pattern
of the field photographs as shown in figure and triggering the SpecificFarmAgent
(Fig. 16).
• The SpecificFarmAgent comes to action and activates the drone agent to determine
causes of the problem by closely investigating the place of issue and constructs
an execution plan to deal with the newly investigated problem.
• The drone and UAVagent preliminarily plans shooting. This is done over the
problem area using hyper spectral cameras as shown in Fig. 1. The whole process
and the related problem and solution is recorded in the Farm Database and also
the experience is registered in the knowledge base (Fig. 17).
• The SpecificFarmAgent sends warning messages to alert the agronomist along
with the photographs taken by the drone (Fig. 18).
• The image recognition API initiates the task of comparing these images with
the images stored in the databases and other approachable knowledge bases.
Accordingly, PPPAgents are activated (Fig. 19).
• By consulting the knowledge base through SAGRO-Lite ontology interface, the
likely reasons of the problem situation are anticipated by coordinated decision
G. K. Shankhdhar et al.
Fig. 12 Generic crop knowledge module
7 Scenario
A “virtual discussion room” in ABSMSA as pointed out earlier is shown with an
example scenario using smart services is considered below:
• The satellite identifies that one of the farms of the horticulturist is abnormal triggered by Supervisor Agent, having a new in-homogeneity by reading the pattern
of the field photographs as shown in figure and triggering the SpecificFarmAgent
(Fig. 16).
• The SpecificFarmAgent comes to action and activates the drone agent to determine
causes of the problem by closely investigating the place of issue and constructs
an execution plan to deal with the newly investigated problem.
• The drone and UAVagent preliminarily plans shooting. This is done over the
problem area using hyper spectral cameras as shown in Fig. 1. The whole process
and the related problem and solution is recorded in the Farm Database and also
the experience is registered in the knowledge base (Fig. 17).
• The SpecificFarmAgent sends warning messages to alert the agronomist along
with the photographs taken by the drone (Fig. 18).
• The image recognition API initiates the task of comparing these images with
the images stored in the databases and other approachable knowledge bases.
Accordingly, PPPAgents are activated (Fig. 19).
• By consulting the knowledge base through SAGRO-Lite ontology interface, the
likely reasons of the problem situation are anticipated by coordinated decision