Robotic System with AI for Real Time Weed Detection, Canopy Aware Spraying, and Droplet Pattern Evaluation
Journal:
arXiv
Published Date:
Jul 7, 2025
Abstract
Uniform and excessive herbicide application in modern agriculture contributes
to increased input costs, environmental pollution, and the emergence of
herbicide resistant weeds. To address these challenges, we developed a vision
guided, AI-driven variable rate sprayer system capable of detecting weed
presence, estimating canopy size, and dynamically adjusting nozzle activation
in real time. The system integrates lightweight YOLO11n and YOLO11n-seg deep
learning models, deployed on an NVIDIA Jetson Orin Nano for onboard inference,
and uses an Arduino Uno-based relay interface to control solenoid actuated
nozzles based on canopy segmentation results. Indoor trials were conducted
using 15 potted Hibiscus rosa sinensis plants of varying canopy sizes to
simulate a range of weed patch scenarios. The YOLO11n model achieved a mean
average precision (mAP@50) of 0.98, with a precision of 0.99 and a recall close
to 1.0. The YOLO11n-seg segmentation model achieved a mAP@50 of 0.48, precision
of 0.55, and recall of 0.52. System performance was validated using water
sensitive paper, which showed an average spray coverage of 24.22% in zones
where canopy was present. An upward trend in mean spray coverage from 16.22%
for small canopies to 21.46% and 21.65% for medium and large canopies,
respectively, demonstrated the system's capability to adjust spray output based
on canopy size in real time. These results highlight the potential of combining
real time deep learning with low-cost embedded hardware for selective herbicide
application. Future work will focus on expanding the detection capabilities to
include three common weed species in South Dakota: water hemp (Amaranthus
tuberculatus), kochia (Bassia scoparia), and foxtail (Setaria spp.), followed
by further validation in both indoor and field trials within soybean and corn
production systems.
