Drone-Based Crop Health Analysis and Precision Agriculture System
Drone-Based Crop Health Analysis and Precision Agriculture System
Agesta Jenifer A1, Muralidharan V 2, Naveen M 3 , Prakash M 4 , Poovarasan J5 , Veeramanikandan U6
1 UG-CSE & Holy Cross Engineering College, Thoothukudi , India
2 PG-MBA & Karpagam College of Engineering – SoMS, Coimbatore, India
3 PG-MBA & Karpagam College of Engineering – SoMS, Coimbatore, India
4 PG-MBA & Karpagam College of Engineering – SoMS, Coimbatore, India
5 PG-MBA & Karpagam College of Engineering – SoMS, Coimbatore, India
6 PG-MBA & Karpagam College of Engineering – SoMS, Coimbatore, India
ABSTRACT - Agriculture remains the backbone of global food security, yet crop diseases, nutrient deficiencies, water stress, and pest infestations cause annual yield losses estimated at 20–40% worldwide. Conventional field scouting methods are labour-intensive, time-consuming, and fail to capture the spatial heterogeneity of large farms. This paper presents a Drone-Based Crop Health Analysis and Precision Agriculture System (DBCHAPS) that integrates multi-spectral and RGB imaging drones, deep learning-based crop disease detection, vegetation index analysis, variable-rate prescription mapping, and autonomous precision spraying. A DJI Matrice 300 RTK drone equipped with a MicaSense RedEdge-MX multi-spectral camera captures high-resolution aerial imagery across five spectral bands (Blue, Green, Red, Red-Edge, Near Infrared). The captured data is processed through a custom-trained YOLOv8-based convolutional neural network (CNN) pipeline to detect 18 distinct crop diseases and stress conditions across rice, wheat, and cotton crops. Concurrently, vegetation indices (NDVI, NDRE, GNDVI, SAVI) are computed to generate prescription maps for site-specific fertilizer and pesticide application. Experimental evaluation on a 120 acre farm in Thanjavur, Tamil Nadu over two crop seasons demonstrates a disease detection accuracy of 96.3%, early stress detection 8–12 days before visible symptoms, and a 31% reduction in agrochemical usage through variable-rate application. The system achieves an end-to-end field analysis time of under 45 minutes for 100 acres.
Keywords — UAV, Precision Agriculture, Crop Disease Detection, Multi-Spectral Imaging, NDVI, YOLOv8, Deep Learning, Variable-Rate Application, Remote Sensing, Smart Farming.