Search Results for author:
Found 20 papers, 6 papers with code
WeedNet: A Foundation Model-Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification
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.
Towards Large Reasoning Models for Agriculture
Using AgThoughts, we develop AgThinker, a suite of small reasoning models that can be run on consumer-grade GPUs, and show that our dataset can be effective in unlocking agricultural reasoning abilities in LLMs.
Optimizing Navigation And Chemical Application in Precision Agriculture With Deep Reinforcement Learning And Conditional Action Tree
This paper presents a novel reinforcement learning (RL)-based planning scheme for optimized robotic management of biotic stresses in precision agriculture.
Soybean Maturity Prediction using 2D Contour Plots from Drone based Time Series Imagery
We utilized contour plot images extracted from the time-series UAV RGB imagery as input for a neural network model.
Robust soybean seed yield estimation using high-throughput ground robot videos
These images are processed through the P2PNet-Yield model, a deep learning framework where we combined a Feature Extraction Module (the backbone of the P2PNet-Soy) and a Yield Regression Module to estimate seed yields of soybean plots.
AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning
We further demonstrate that deep reinforcement learning (RL) policies can be trained using AgGym for designing ultra-precise biotic stress mitigation strategies with potential to increase yield recovery with less chemicals and lower cost.
AgEval: A Benchmark for Zero-Shot and Few-Shot Plant Stress Phenotyping with Multimodal LLMs
Plant stress phenotyping traditionally relies on expert assessments and specialized models, limiting scalability in agriculture.
BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity
We introduce BioTrove, the largest publicly accessible dataset designed to advance AI applications in biodiversity.
Class-specific Data Augmentation for Plant Stress Classification
This research represents an advancement in automated data augmentation strategies for plant stress classification, particularly in the context of confounding datasets.
Multi-Sensor and Multi-temporal High-Throughput Phenotyping for Monitoring and Early Detection of Water-Limiting Stress in Soybean
We investigated a set of diverse soybean accessions using multiple sensors in a time series high-throughput phenotyping manner to: (1) develop a pipeline for rapid classification of soybean drought stress symptoms, and (2) investigate methods for early detection of drought stress.
Deep learning powered real-time identification of insects using citizen science data
InsectNet can guide citizen science data collection, especially for invasive species where early detection is crucial.
Out-of-distribution detection algorithms for robust insect classification
One of the primary emphasis of researchers is to implement identification and classification models in the real agriculture fields, which is challenging because input images that are wildly out of the distribution (e. g., images like vehicles, animals, humans, or a blurred image of an insect or insect class that is not yet trained on) can produce an incorrect insect classification.
SUTRA: A Novel Approach to Modelling Pandemics with Applications to COVID-19
The Covid-19 pandemic has two key properties: (i) asymptomatic cases (both detected and undetected) that can result in new infections, and (ii) time-varying characteristics due to new variants, Non-Pharmaceutical Interventions etc.
Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications
The objective of this study is to develop a machine learning (ML) approach adept at soybean [\textit{Glycine max} L.
Usefulness of interpretability methods to explain deep learning based plant stress phenotyping
For some images, the output of the interpretability methods indicated that spurious feature correlations may have been used to correctly classify them.
How useful is Active Learning for Image-based Plant Phenotyping?
To overcome this challenge, active learning algorithms have been proposed that reduce the amount of labeling needed by deep learning models to achieve good predictive performance.
Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps
We identify the most sensitive wavelength as 733 nm using the saliency map visualization.
Interpretable Deep Learning applied to Plant Stress Phenotyping
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 minute traits in applicable fields of biological sciences, is scarce.
Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean
The focus of this work is to determine the minimal number of most effective hyperspectral bands that can distinguish between healthy and diseased specimens early in the growing season.
An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection
This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks.
