WeedNet: A Foundation Model-Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification
Journal:
arXiv
Published Date:
May 25, 2025
Abstract
Early identification of weeds is essential for effective management and
control, and there is growing interest in automating the process using computer
vision techniques coupled with AI methods. However, challenges associated with
training AI-based weed identification models, such as limited expert-verified
data and complexity and variability in morphological features, have hindered
progress. To address these issues, we present WeedNet, the first global-scale
weed identification model capable of recognizing an extensive set of weed
species, including noxious and invasive plant species. WeedNet is an end-to-end
real-time weed identification pipeline and uses self-supervised learning,
fine-tuning, and enhanced trustworthiness strategies. WeedNet achieved 91.02%
accuracy across 1,593 weed species, with 41% species achieving 100% accuracy.
Using a fine-tuning strategy and a Global-to-Local approach, the local Iowa
WeedNet model achieved an overall accuracy of 97.38% for 85 Iowa weeds, most
classes exceeded a 90% mean accuracy per class. Testing across intra-species
dissimilarity (developmental stages) and inter-species similarity (look-alike
species) suggests that diversity in the images collected, spanning all the
growth stages and distinguishable plant characteristics, is crucial in driving
model performance. The generalizability and adaptability of the Global WeedNet
model enable it to function as a foundational model, with the Global-to-Local
strategy allowing fine-tuning for region-specific weed communities. Additional
validation of drone- and ground-rover-based images highlights the potential of
WeedNet for integration into robotic platforms. Furthermore, integration with
AI for conversational use provides intelligent agricultural and ecological
conservation consulting tools for farmers, agronomists, researchers, land
managers, and government agencies across diverse landscapes.
