Precision Agriculture, Crop Monitoring, and Yield Prediction
Module: Sustainable Tech | Difficulty: Premium
Crop Yield Model
NDVI Calculation
Comparison
| Parameter | Optimal Range | Impact on Yield | Measurement Method |
|---|---|---|---|
| pH | 6.0-7.5 | High | Soil probe |
| Organic matter | 3-5% | High | Loss on ignition |
| Nitrogen | 40-60 ppm | Very high | Kjeldahl |
| Phosphorus | 25-50 ppm | Medium | Olsen method |
Python Implementation
import numpy as np
from sklearn.ensemble import RandomForestRegressor
class PrecisionAgriculture:
def __init__(self):
self.model = RandomForestRegressor(n_estimators=100)
def calculate_ndvi(self, nir_band, red_band):
return (nir_band - red_band) / (nir_band + red_band + 1e-10)
def water_stress_index(self, actual_et, potential_et):
return np.clip(actual_et / (potential_et + 1e-10), 0, 1)
def nitrogen_recommendation(self, soil_n, crop_type, growth_stage):
reqs = {'wheat': [30, 60, 90], 'corn': [50, 100, 150], 'soybean': [20, 40, 60]}
return max(0, reqs[crop_type][growth_stage] - soil_n) * 1.5
Research Insight: Satellite-based crop monitoring can detect water stress 2-3 weeks before visible symptoms appear, reducing water use by 30-50%.