Asheesh K. Singh

Orcid: 0000-0003-3722-1045

According to our database1, Asheesh K. Singh authored at least 21 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
AgEval: A Benchmark for Zero-Shot and Few-Shot Plant Stress Phenotyping with Multimodal LLMs.
CoRR, 2024

Arboretum: A Large Multimodal Dataset Enabling AI for Biodiversity.
CoRR, 2024

Class-specific Data Augmentation for Plant Stress Classification.
CoRR, 2024

Multi-Sensor and Multi-temporal High-Throughput Phenotyping for Monitoring and Early Detection of Water-Limiting Stress in Soybean.
CoRR, 2024

AIIRA: AI Institute for Resilient Agriculture.
AI Mag., 2024

2023
Multi-objective planning of electric vehicles charging in distribution system considering priority-based vehicle-to-grid scheduling.
Swarm Evol. Comput., March, 2023

Smart Connected Farms and Networked Farmers to Tackle Climate Challenges Impacting Agricultural Production.
CoRR, 2023

Deep learning powered real-time identification of insects using citizen science data.
CoRR, 2023

Out-of-distribution detection algorithms for robust insect classification.
CoRR, 2023

2022
Dataset Documenting the Interactions of Biochar with Manure, Soil, and Plants: Towards Improved Sustainability of Animal and Crop Agriculture.
Data, 2022

2020
Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications.
CoRR, 2020

Usefulness of interpretability methods to explain deep learning based plant stress phenotyping.
CoRR, 2020

Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning.
CoRR, 2020

How useful is Active Learning for Image-based Plant Phenotyping?
CoRR, 2020

2018
A Novel Multirobot System for Plant Phenotyping.
Robotics, 2018

An explainable deep machine vision framework for plant stress phenotyping.
Proc. Natl. Acad. Sci. USA, 2018

Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps.
CoRR, 2018


2017
Interpretable Deep Learning applied to Plant Stress Phenotyping.
CoRR, 2017

Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean.
CoRR, 2017

2016
An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection.
CoRR, 2016


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