3. Advanced Reasoning & Rules

Implementing SWRL rules and SPARQL for intelligent diagnosis

Important

Integration Note: This section connects our ontology with the Plant Pathology Ontology (PPO) and AGROVOC for comprehensive disease reasoning. We’ll use the same disease examples from our previous ontology.

Advanced Reasoning in Protégé

1. Advanced Class Expressions for Plant Pathology

Integrating PPIO and Plant Ontology

Complex Class Definitions

# A plant with high disease severity that affects leaves
HighRiskPlant ≡ Plant ⊓ 
    (hasDisease some (severity some xsd:float[>= 7.0])) ⊓
    (hasDisease some (hasSymptom some (location "Leaf")))

# A fungal disease that appears in humid conditions
HumidConditionDisease ≡ FungalDisease ⊓
    (preferredHumidity some xsd:float[> 70.0])

2. SWRL Rules for Plant Disease Diagnosis

PPIO-Inspired Rules for Host-Pathogen Interaction

# PPIO Rule 1: Host-Pathogen Interaction
Plant(?p) ∧ Pathogen(?path) ∧ hasPathogen(?p, ?path) ∧
hasDisease(?p, ?d) ∧ causedBy(?d, ?path) → hasActiveInfection(?p, true)

# PPIO Rule 2: Resistance Mechanism
Plant(?p) ∧ hasResistanceGene(?p, ?gene) ∧
Disease(?d) ∧ hasResistanceGene(?d, ?gene) → resistantTo(?p, ?d)

# PPIO Rule 3: Environmental Influence
Disease(?d) ∧ hasOptimalTemp(?d, ?optTemp) ∧
EnvironmentalCondition(?e) ∧ hasTemperature(?e, ?temp) ∧
swrlb:greaterThan(?temp, ?optTemp + 5) → decreasesSeverity(?d, 0.5)

CropPest-Inspired IPM Rules

# Economic Threshold Rule
Pest(?p) ∧ Crop(?c) ∧ affects(?p, ?c) ∧
hasPopulation(?p, ?pop) ∧ hasEconomicThreshold(?p, ?threshold) ∧
swrlb:greaterThan(?pop, ?threshold) → requiresTreatment(?c, true)

# Biological Control Rule
Pest(?p) ∧ NaturalEnemy(?ne) ∧ preysOn(?ne, ?p) ∧
hasPopulation(?p, ?pop) ∧ swrlb:greaterThan(?pop, 10) →
recommendTreatment('BiologicalControl', ?ne)

Plant Ontology-Enhanced Symptom Rules

Basic Symptom-Based Rules

# Rule 1: Yellow spots + white powder → Powdery Mildew
Plant(?p) ∧ hasSymptom(?p, ?s1) ∧ hasSymptom(?p, ?s2) ∧
YellowSpots(?s1) ∧ WhitePowder(?s2) → hasDisease(?p, PowderyMildew)

# Rule 2: Wilting + yellow leaves → Possible Fusarium Wilt
Plant(?p) ∧ hasSymptom(?p, ?s1) ∧ hasSymptom(?p, ?s2) ∧
Wilting(?s1) ∧ YellowLeaves(?s2) ∧ 
hasTemperature(?p, ?t) ∧ swrlb:greaterThan(?t, 25) →
suspectedDisease(?p, FusariumWilt)

Environmental Condition Rules

# High humidity increases fungal disease risk
Plant(?p) ∧ hasEnvironment(?p, ?e) ∧ 
hasHumidity(?e, ?h) ∧ swrlb:greaterThan(?h, 80) ∧
hasDisease(?p, ?d) ∧ FungalDisease(?d) →
increaseSeverity(?d, 1.5)

# Hot and dry conditions favor bacterial diseases
Plant(?p) ∧ hasEnvironment(?p, ?e) ∧
hasTemperature(?e, ?t) ∧ swrlb:greaterThan(?t, 30) ∧
hasHumidity(?e, ?h) ∧ swrlb:lessThan(?h, 40) ∧
hasDisease(?p, ?d) ∧ BacterialDisease(?d) →
increaseSeverity(?d, 2.0)

3. SPARQL Queries for Plant Disease Knowledge Extraction

Diagnostic Queries with AGROVOC Integration

Finding High-Risk Plants with Environmental Factors

PREFIX plant: <https://ontologies.mn-ai.guru/plant-disease#>

SELECT ?plant ?disease ?severity
WHERE {
    ?plant a plant:Plant ;
           plant:hasDisease ?disease .
    ?disease plant:severity ?severity .
    FILTER (?severity > 7.0)
}
ORDER BY DESC(?severity)

Multilingual Treatment Recommendations with AGROVOC

PREFIX plant: <https://ontologies.mn-ai.guru/plant-disease#>

SELECT ?disease ?treatment ?effectiveness
WHERE {
    ?disease a plant:Disease ;
             plant:name ?diseaseName ;
             plant:treatedWith ?treatment .
    ?treatment plant:effectiveness ?effectiveness .
    FILTER (?effectiveness > 0.7)
}
ORDER BY DESC(?effectiveness)

4. Practical Exercise: Build a Diagnostic Knowledge Base

Exercise 1: Implement SWRL Rules

  1. Create a rule that identifies high-risk wheat fields based on:
    • Current weather conditions
    • Growth stage (using Plant Ontology)
    • Previous disease history
  2. Develop a rule that suggests IPM strategies considering:
    • Pest population thresholds
    • Crop growth stage
    • Environmental conditions
    • Available natural enemies

Exercise 2: SPARQL for Decision Support

  1. Write a query that finds all wheat diseases active in:
    • Current temperature range
    • Current humidity range
    • Current growth stage
  2. Create a query that recommends treatments based on:
    • Disease type and severity
    • Crop growth stage
    • Environmental conditions
    • Available control methods
  3. Set up rules for 3 common plant diseases
  4. Create SPARQL queries to:
    • Find all high-risk plants
    • List effective treatments
    • Identify environmental risk factors
  5. Test your rules with sample data

5. Debugging and Optimizing Reasoning

Common Issues in Plant Disease Ontologies

  1. Inconsistent Disease Classification
    • Symptom overlaps between diseases
    • Conflicting treatment recommendations
    • Ambiguous severity scales
  2. Performance Optimization
    • Index frequently queried properties
    • Use SPARQL for complex queries instead of reasoning
    • Modularize large ontologies

Common issues and solutions:

  1. Inconsistent Ontology
    • Check for conflicting class definitions
    • Verify property domains/ranges
    • Use Protégé’s explanation feature
  2. Rules Not Firing
    • Check for typos in class/property names
    • Verify all required conditions are met
    • Test with simpler rules first
  3. Performance Issues
    • Optimize complex class expressions
    • Consider using SPARQL for complex queries
    • Increase JVM heap size if needed

Next Steps: Python Integration for Intelligent Diagnosis

In the next section, we’ll:

  1. Load and query our ontology using OWLReady2
  2. Build a Python-based diagnostic system
  3. Integrate with environmental sensors
  4. Connect to LLMs for natural language queries
  5. Prepare for MOE architecture integration

This will transform our ontology into a powerful diagnostic tool that can power intelligent agricultural systems.