SAGRO-Lite: A Light Weight Agent Based Semantic Model …
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• What are the reasonable harvests or crops that are to be grown?
• The choice of crops.
• Selection of manures.
• Selection of time to use manure.
• Identification of pests and plant infections.
• Disease preventive measures.
• Appropriate techniques to counter particular disease.
• Side effects of a particular disease.
• Significant steps to keep up nature of crop harvestings.
• Consideration for post harvest methods.
• Harvests developed by different ranchers and quantity.
The next task is to identify broad areas of cultivation to figure out the details
regarding the above identified points. These include nurseries, harvests and post
harvests considerations including pest control, fertilizers and common control tasks.
To semantically annotate data streams, SAGRO-Lite works in collaboration with
IOT-Lite, a light-weight information model developed on top of SSN [73–76].
By providing an RDF-based representation of heterogeneous streams, C-SPARQL
solves the challenge of giving reasoners an access protocol for heterogeneous streams
[77, 78]. As RDF is the most accepted format to feed information to reasoners, CSPARQL allows existing reasoning mechanisms to be further extended in order to
support continuous reasoning over data streams and rich background knowledge. CSPARQL is excellent to be used in complex and multi-stream queries while CQELS
is primarily used in queries requiring static data.
The logical idea behind designing this ontology was that the Indian farmer needs
knowledge regarding crops, climate, humidity, soil condition, pests and diseases only
under the boundary of his agricultural needs and cultivation. So monolithic datasets
describing about useless crops, yields, soil, optimum moisture levels, temperature
and knowledge about related paraphernalia clearly is not needed. This also increases
the functioning and performance of the eco-system.
The SAGRO-Lite ontology is derived from the Generic Crop Knowledge Module
shown in Fig. 12. Centered on crop, its related entities are basic characteristics,
climate, fertilizers, disease, symptoms, cure, harvesting, marketing and economics.
An extract of this ontology developed in Protégé 5.2 in the form of an OWLViz diagram is shown in Fig. 13. The entity crop is expanded to clearly depict crop
type, common name, scientific name, etc. shown in Fig. 14. Then to another level,
the ontology is further expanded where we can see the details and relationships of
the attributes of ‘Wheat’ crop with hasMinTemp, hasMaxTemp, hasMinRainfall,
hasMaxRainfall, hasSoilType, hasDisease, etc. shown in Fig. 15.
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• What are the reasonable harvests or crops that are to be grown?
• The choice of crops.
• Selection of manures.
• Selection of time to use manure.
• Identification of pests and plant infections.
• Disease preventive measures.
• Appropriate techniques to counter particular disease.
• Side effects of a particular disease.
• Significant steps to keep up nature of crop harvestings.
• Consideration for post harvest methods.
• Harvests developed by different ranchers and quantity.
The next task is to identify broad areas of cultivation to figure out the details
regarding the above identified points. These include nurseries, harvests and post
harvests considerations including pest control, fertilizers and common control tasks.
To semantically annotate data streams, SAGRO-Lite works in collaboration with
IOT-Lite, a light-weight information model developed on top of SSN [73–76].
By providing an RDF-based representation of heterogeneous streams, C-SPARQL
solves the challenge of giving reasoners an access protocol for heterogeneous streams
[77, 78]. As RDF is the most accepted format to feed information to reasoners, CSPARQL allows existing reasoning mechanisms to be further extended in order to
support continuous reasoning over data streams and rich background knowledge. CSPARQL is excellent to be used in complex and multi-stream queries while CQELS
is primarily used in queries requiring static data.
The logical idea behind designing this ontology was that the Indian farmer needs
knowledge regarding crops, climate, humidity, soil condition, pests and diseases only
under the boundary of his agricultural needs and cultivation. So monolithic datasets
describing about useless crops, yields, soil, optimum moisture levels, temperature
and knowledge about related paraphernalia clearly is not needed. This also increases
the functioning and performance of the eco-system.
The SAGRO-Lite ontology is derived from the Generic Crop Knowledge Module
shown in Fig. 12. Centered on crop, its related entities are basic characteristics,
climate, fertilizers, disease, symptoms, cure, harvesting, marketing and economics.
An extract of this ontology developed in Protégé 5.2 in the form of an OWLViz diagram is shown in Fig. 13. The entity crop is expanded to clearly depict crop
type, common name, scientific name, etc. shown in Fig. 14. Then to another level,
the ontology is further expanded where we can see the details and relationships of
the attributes of ‘Wheat’ crop with hasMinTemp, hasMaxTemp, hasMinRainfall,
hasMaxRainfall, hasSoilType, hasDisease, etc. shown in Fig. 15.