Drone-Based Crop Health Monitoring System

Solution for the use case Smart Agriculture Crop Monitoring with Drones.

AI tools

OpenCV, NDVI, Drone SDK, Python, GIS, Machine Learning

Language

Python

Accuracy

92%

Published

2025-07-07

Code (Python)

import cv2
import numpy as np
from sklearn.cluster import KMeans

class CropHealthAnalyzer:
    def __init__(self):
        self.ndvi_threshold = 0.3
        
    def calculate_ndvi(self, nir_band, red_band):
        # Normalized Difference Vegetation Index
        ndvi = (nir_band - red_band) / (nir_band + red_band + 0.001)
        return np.clip(ndvi, -1, 1)
        
    def detect_disease(self, rgb_image):
        # Convert to HSV for better color analysis
        hsv = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2HSV)
        
        # Define color ranges for disease symptoms
        yellow_mask = cv2.inRange(hsv, (20, 100, 100), (30, 255, 255))
        brown_mask = cv2.inRange(hsv, (10, 50, 20), (20, 255, 200))
        
        disease_area = cv2.bitwise_or(yellow_mask, brown_mask)
        return np.sum(disease_area) / disease_area.size
        
    def recommend_action(self, ndvi_map, disease_severity):
        if np.mean(ndvi_map) < self.ndvi_threshold:
            return "Immediate irrigation required"
        elif disease_severity > 0.1:
            return "Apply fungicide treatment"
        else:
            return "Crop health normal"

Prompts

Analyze this crop field imagery: NDVI values below 0.3 in the northwest section, browning edges on leaves, and scattered yellow patches. Recommend immediate irrigation and possible fungicide treatment.