5 papers
TabQL: In-Context Q-Learning with Tabular Foundation Models
Qisai Liu, Zhanhong Jiang, Timilehin Ayanlade +4
We propose Tabular Q-Learning (TabQL), a reinforcement learning framework that replaces the conventional parametric Q-network in Deep Q-Learning (DQN) with a tabular foundation mod…
Crossmodal learning for Crop Canopy Trait Estimation
Timilehin T. Ayanlade, Anirudha Powadi, Talukder Z. Jubery +2
Recent advances in plant phenotyping have driven widespread adoption of multi sensor platforms for collecting crop canopy reflectance data. This includes the collection of heteroge…
WeedNet: A Foundation Model-Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification
Yanben Shen, Timilehin T. Ayanlade, Venkata Naresh Boddepalli +13
Early weed identification is crucial for effective management and control, and researchers, agronomists, and technology developers are increasingly interested in automating this pr…
Robust soybean seed yield estimation using high-throughput ground robot videos
Jiale Feng, Samuel W. Blair, Timilehin Ayanlade +5
We present a novel method for soybean (Glycine max (L.) Merr.) yield estimation leveraging high throughput seed counting via computer vision and deep learning techniques. Tradition…
Multi-Sensor and Multi-temporal High-Throughput Phenotyping for Monitoring and Early Detection of Water-Limiting Stress in Soybean
Sarah E. Jones, Timilehin Ayanlade, Benjamin Fallen +5
Soybean production is susceptible to biotic and abiotic stresses, exacerbated by extreme weather events. Water limiting stress, i.e. drought, emerges as a significant risk for soyb…