farming-expert

name farming-expert version 1.0.0 description Expert-level precision agriculture, farm management systems, crop monitoring, and agtech category domains tags ["agriculture","farming","precision-agriculture","agtech","crop-management"] allowed-tools ["Read","Write","Edit"] 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 } IoT Sensor Integration 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" ]: .1 f} ' ) 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' } Weather and Climate Analysis 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]) } Pest and Disease Management 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' ]) } Best Practices Use precision agriculture techniques Implement crop rotation Monitor soil health regularly Integrate weather data for decisions Use IoT sensors for real-time monitoring Apply variable rate technology Optimize water usage Practice integrated pest management Track field-level profitability Use data-driven decision making Maintain equipment properly Follow sustainable practices Anti-Patterns ❌ 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 Resources Precision Agriculture: https://www.ispag.org/ FAO Agricultural Data: http://www.fao.org/faostat/ Crop Science Society: https://www.crops.org/ Agricultural IoT: https://www.iot-agriculturet.com/