35 citations · 73 across the 17 of their papers we have counts for
18 papers
SAGE: Scalable Agentic Grounded Evaluation for Crop Disease Diagnosis
Muhammad Arbab Arshad, Tirtho Roy, Yanben Shen +7
Plant disease diagnosis is critical for food security, yet training disease-recognition models that generalize across crops, pathogens, and field conditions remains challenging bec…
FloraForge: LLM-Assisted Procedural Generation of Editable and Analysis-Ready 3D Plant Geometric Models For Agricultural Applications
Mozhgan Hadadi, Talukder Z. Jubery, Patrick S. Schnable +4
Accurate 3D plant models are crucial for computational phenotyping and physics-based simulation; however, current approaches face significant limitations. Learning-based reconstruc…
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…
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…
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…
Leveraging Vision Language Models for Specialized Agricultural Tasks
Muhammad Arbab Arshad, Talukder Zaki Jubery, Tirtho Roy +8
As Vision Language Models (VLMs) become increasingly accessible to farmers and agricultural experts, there is a growing need to evaluate their potential in specialized tasks. We pr…
Class-specific Data Augmentation for Plant Stress Classification
Nasla Saleem, Aditya Balu, Talukder Zaki Jubery +4
Data augmentation is a powerful tool for improving deep learning-based image classifiers for plant stress identification and classification. However, selecting an effective set of…
BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity
Chih-Hsuan Yang, Benjamin Feuer, Zaki Jubery +12
We introduce BioTrove, the largest publicly accessible dataset designed to advance AI applications in biodiversity. Curated from the iNaturalist platform and vetted to include only…
Deep learning powered real-time identification of insects using citizen science data
Shivani Chiranjeevi, Mojdeh Sadaati, Zi K Deng +10
Insect-pests significantly impact global agricultural productivity and quality. Effective management involves identifying the full insect community, including beneficial insects an…
Out-of-distribution detection algorithms for robust insect classification
Mojdeh Saadati, Aditya Balu, Shivani Chiranjeevi +5
Deep learning-based approaches have produced models with good insect classification accuracy; Most of these models are conducive for application in controlled environmental conditi…
Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications
Luis G Riera, Matthew E. Carroll, Zhisheng Zhang +8
Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop…
Usefulness of interpretability methods to explain deep learning based plant stress phenotyping
Koushik Nagasubramanian, Asheesh K. Singh, Arti Singh +2
Deep learning techniques have been successfully deployed for automating plant stress identification and quantification. In recent years, there is a growing push towards training mo…
How useful is Active Learning for Image-based Plant Phenotyping?
Koushik Nagasubramanian, Talukder Z. Jubery, Fateme Fotouhi Ardakani +5
Deep learning models have been successfully deployed for a diverse array of image-based plant phenotyping applications including disease detection and classification. However, succ…
Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps
Koushik Nagasubramanian, Sarah Jones, Asheesh K. Singh +3
Our overarching goal is to develop an accurate and explainable model for plant disease identification using hyperspectral data. Charcoal rot is a soil borne fungal disease that aff…
Interpretable Deep Learning applied to Plant Stress Phenotyping
Sambuddha Ghosal, David Blystone, Asheesh K. Singh +3
Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and…
Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean
Koushik Nagasubramanian, Sarah Jones, Soumik Sarkar +3
Charcoal rot is a fungal disease that thrives in warm dry conditions and affects the yield of soybeans and other important agronomic crops worldwide. There is a need for robust, au…
An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection
Adedotun Akintayo, Nigel Lee, Vikas Chawla +7
This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks. The crux of the idea is to train a deep convolutional autoencoder to…