Expert-level precision agriculture, farm management systems, crop monitoring, and agtech
Expert guidance for precision agriculture, farm management systems, crop monitoring, IoT sensors, and agricultural technology.
from dataclasses import dataclass
from typing import List, Optional
from datetime import datetime, timedelta
from enum import Enum
class CropType(Enum):
WHEAT = "wheat"
CORN = "corn"
SOYBEANS = "soybeans"
RICE = "rice"
VEGETABLES = "vegetables"
class GrowthStage(Enum):
PLANTED = "planted"
GERMINATION = "germination"
VEGETATIVE = "vegetative"
FLOWERING = "flowering"
HARVEST_READY = "harvest_ready"
HARVESTED = "harvested"
@dataclass
class Field:
field_id: str
name: str
area_hectares: float
soil_type: str
coordinates: List[tuple] # GPS polygon
irrigation_system: str
drainage_quality: str
@dataclass
class CropCycle:
cycle_id: str
field_id: str
crop_type: CropType
variety: str
planting_date: datetime
expected_harvest_date: datetime
growth_stage: GrowthStage
seed_rate: float
fertilizer_applied: List[dict]
pesticides_applied: List[dict]
irrigation_schedule: List[dict]
class FarmManagementSystem:
"""Farm management and crop tracking"""
def __init__(self, db):
self.db = db
def plan_crop_rotation(self, field_id, years=3):
"""Generate crop rotation plan"""
field = self.db.get_field(field_id)
history = self.db.get_crop_history(field_id, years=10)
# Analyze soil nutrients and previous crops
rotation_plan = []
# Rules for rotation:
# - Alternate nitrogen-fixing and nitrogen-demanding crops
# - Avoid same crop family consecutively
# - Consider soil health and pest management
for year in range(years):
recommended_crop = self.recommend_next_crop(field, history, year)
rotation_plan.append({
'year': datetime.now().year + year,
'crop': recommended_crop,
'reason': self.explain_recommendation(recommended_crop, history)
})
return rotation_plan
def monitor_crop_health(self, field_id):
"""Monitor crop health using sensor data"""
field = self.db.get_field(field_id)
current_crop = self.db.get_current_crop(field_id)
# Collect sensor data
soil_moisture = self.get_soil_moisture_data(field_id)
weather_data = self.get_weather_data(field.coordinates)
ndvi_data = self.get_ndvi_from_satellite(field.coordinates)
# Analyze health indicators
health_score = self.calculate_health_score(
soil_moisture,
weather_data,
ndvi_data,
current_crop
)
alerts = []
if soil_moisture < current_crop.optimal_moisture_min:
alerts.append({
'type': 'irrigation_needed',
'severity': 'high',
'message': 'Soil moisture below optimal level'
})
if ndvi_data < 0.6: # Vegetation health threshold
alerts.append({
'type': 'crop_stress',
'severity': 'medium',
'message': 'NDVI indicates possible crop stress'
})
return {
'field_id': field_id,
'health_score': health_score,
'soil_moisture': soil_moisture,
'ndvi': ndvi_data,
'alerts': alerts,
'recommendations': self.generate_recommendations(alerts)
}
def predict_yield(self, field_id):
"""Predict crop yield using ML"""
field = self.db.get_field(field_id)
current_crop = self.db.get_current_crop(field_id)
# Features for prediction
features = {
'field_area': field.area_hectares,
'soil_type': field.soil_type,
'crop_variety': current_crop.variety,
'days_since_planting': (datetime.now() - current_crop.planting_date).days,
'total_rainfall': self.get_accumulated_rainfall(field_id),
'avg_temperature': self.get_avg_temperature(field_id),
'fertilizer_amount': sum(f['amount'] for f in current_crop.fertilizer_applied),
'ndvi_avg': self.get_avg_ndvi(field_id)
}
# Use trained model to predict yield
predicted_yield_per_hectare = self.yield_model.predict([features])[0]
total_yield = predicted_yield_per_hectare * field.area_hectares
return {
'field_id': field_id,
'predicted_yield_kg': total_yield,
'yield_per_hectare': predicted_yield_per_hectare,
'confidence': 0.85,
'expected_harvest_date': current_crop.expected_harvest_date
}
class AgricultureIoT:
"""IoT sensor data collection and analysis"""
def process_soil_sensor_data(self, sensor_id):
"""Process soil sensor readings"""
readings = self.db.get_recent_readings(sensor_id, hours=24)
analysis = {
'sensor_id': sensor_id,
'avg_moisture': np.mean([r['moisture'] for r in readings]),
'avg_temperature': np.mean([r['temperature'] for r in readings]),
'avg_ph': np.mean([r['ph'] for r in readings]),
'avg_ec': np.mean([r['ec'] for r in readings]), # Electrical conductivity
'nitrogen_level': np.mean([r['nitrogen'] for r in readings]),
'phosphorus_level': np.mean([r['phosphorus'] for r in readings]),
'potassium_level': np.mean([r['potassium'] for r in readings])
}
# Detect anomalies
anomalies = []
if analysis['avg_moisture'] < 20:
anomalies.append('Low soil moisture - irrigation recommended')
if analysis['avg_ph'] < 5.5 or analysis['avg_ph'] > 7.5:
anomalies.append(f'Soil pH out of optimal range: {analysis["avg_ph"]:.1f}')
analysis['anomalies'] = anomalies
return analysis
def automate_irrigation(self, field_id):
"""Automated irrigation control"""
field = self.db.get_field(field_id)
soil_moisture = self.get_soil_moisture_data(field_id)
weather_forecast = self.get_weather_forecast(field.coordinates, days=3)
# Decision logic
should_irrigate = False
duration_minutes = 0
# Check if irrigation is needed
if soil_moisture < field.moisture_threshold:
# Check if rain is expected
expected_rainfall = sum(day['rainfall_mm'] for day in weather_forecast)
if expected_rainfall < 10: # Less than 10mm expected
should_irrigate = True
# Calculate irrigation duration
moisture_deficit = field.moisture_threshold - soil_moisture
duration_minutes = int(moisture_deficit * field.area_hectares * 60 / field.irrigation_rate)
if should_irrigate:
self.activate_irrigation(field_id, duration_minutes)
return {
'field_id': field_id,
'irrigation_activated': should_irrigate,
'duration_minutes': duration_minutes,
'reason': 'Soil moisture below threshold' if should_irrigate else 'No irrigation needed'
}
class WeatherAnalytics:
"""Weather-based agricultural decisions"""
def analyze_growing_conditions(self, field_id, date_range):
"""Analyze weather suitability for crops"""
weather_data = self.get_historical_weather(field_id, date_range)
# Calculate growing degree days (GDD)
gdd = sum([
max(0, (day['temp_max'] + day['temp_min']) / 2 - 10)
for day in weather_data
])
# Analyze frost risk
frost_days = len([d for d in weather_data if d['temp_min'] < 0])
# Water balance
total_rainfall = sum(d['rainfall_mm'] for d in weather_data)
total_evapotranspiration = sum(d['et_mm'] for d in weather_data)
water_deficit = total_evapotranspiration - total_rainfall
return {
'growing_degree_days': gdd,
'frost_days': frost_days,
'total_rainfall_mm': total_rainfall,
'water_deficit_mm': water_deficit,
'avg_temperature': np.mean([d['temp_avg'] for d in weather_data]),
'suitability_score': self.calculate_suitability_score(gdd, frost_days, water_deficit)
}
def optimize_planting_date(self, field_id, crop_type):
"""Determine optimal planting date"""
historical_weather = self.get_historical_weather(field_id, years=10)
crop_requirements = self.get_crop_requirements(crop_type)
# Find window with:
# - Minimal frost risk
# - Adequate soil temperature
# - Good rainfall distribution
optimal_dates = []
for year_data in historical_weather:
for date, conditions in year_data.items():
score = self.score_planting_conditions(
conditions,
crop_requirements
)
optimal_dates.append((date, score))
# Return best planting window
best_dates = sorted(optimal_dates, key=lambda x: x[1], reverse=True)[:10]
return {
'recommended_planting_window': {
'start': best_dates[-1][0],
'end': best_dates[0][0]
},
'confidence': np.mean([d[1] for d in best_dates])
}
class PestManagement:
"""Pest and disease monitoring and management"""
def detect_pest_risk(self, field_id):
"""Predict pest pressure"""
weather_data = self.get_recent_weather(field_id, days=14)
crop = self.db.get_current_crop(field_id)
# Environmental factors affecting pests
avg_temp = np.mean([d['temperature'] for d in weather_data])
avg_humidity = np.mean([d['humidity'] for d in weather_data])
rainfall = sum(d['rainfall_mm'] for d in weather_data)
risk_factors = {
'temperature_risk': self.assess_temp_risk(avg_temp, crop.crop_type),
'humidity_risk': self.assess_humidity_risk(avg_humidity),
'rainfall_risk': self.assess_rainfall_risk(rainfall)
}
overall_risk = sum(risk_factors.values()) / len(risk_factors)
recommendations = []
if overall_risk > 0.7:
recommendations.append('Scout fields for pest activity')
recommendations.append('Consider preventive treatment')
elif overall_risk > 0.5:
recommendations.append('Increase monitoring frequency')
return {
'field_id': field_id,
'overall_risk_score': overall_risk,
'risk_level': 'high' if overall_risk > 0.7 else 'medium' if overall_risk > 0.4 else 'low',
'risk_factors': risk_factors,
'recommendations': recommendations
}
def analyze_crop_image(self, image_data):
"""Detect diseases from crop images using ML"""
# Would use computer vision model (CNN) trained on crop diseases
# Returns detected diseases and confidence scores
predictions = self.disease_detection_model.predict(image_data)
return {
'detected_diseases': predictions['diseases'],
'confidence': predictions['confidence'],
'affected_area_percent': predictions['affected_area'],
'treatment_recommendations': self.get_treatment_plan(predictions['diseases'])
}
❌ Over-application of inputs ❌ Ignoring soil health ❌ No crop rotation ❌ Manual data collection only ❌ Ignoring weather forecasts ❌ Reactive instead of proactive management ❌ No yield analysis