most citedAgGym: An agricultural biotic stress simulation environment for ultra-precision management planning

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025

ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculture

Vishesh Kumar Tanwar, Soumik Sarkar, Asheesh K. Singh +1

To empower precision agriculture through distributed machine learning (DML), split learning (SL) has emerged as a promising paradigm, partitioning deep neural networks (DNNs) betwe…

cs.CV2025

TerraIncognita: A Dynamic Benchmark for Species Discovery Using Frontier Models

Shivani Chiranjeevi, Hossein Zaremehrjerdi, Zi K. Deng +9

The rapid global loss of biodiversity, particularly among insects, represents an urgent ecological crisis. Current methods for insect species discovery are manual, slow, and severe…

cs.LG20251 cited

Towards Large Reasoning Models for Agriculture

Hossein Zaremehrjerdi, Shreyan Ganguly, Ashlyn Rairdin +17

Agricultural decision-making involves complex, context-specific reasoning, where choices about crops, practices, and interventions depend heavily on geographic, climatic, and econo…

cs.CV2024

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…

cs.AI20242 cited

AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning

Mahsa Khosravi, Matthew Carroll, Kai Liang Tan +8

Agricultural production requires careful management of inputs such as fungicides, insecticides, and herbicides to ensure a successful crop that is high-yielding, profitable, and of…