farming-expert

Farming Expert

Expert guidance for precision agriculture, farm management systems, crop monitoring, IoT sensors, and agricultural technology.

Core Concepts

Precision Agriculture

• GPS-guided equipment
• Variable rate technology
• Crop monitoring and sensors
• Soil analysis and mapping
• Drone/satellite imagery
• Automated irrigation systems

Farm Management

• Crop planning and rotation
• Resource optimization
• Yield prediction
• Weather forecasting integration
• Equipment maintenance
• Financial management

AgTech Solutions

• IoT sensors (soil, weather)
• Machine learning for yield prediction
• Automated harvesting
• Livestock tracking
• Supply chain integration
• Marketplace platforms

Farm Management System

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
        }