A5062377487- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Smart Agriculture and AI
- Spectroscopy and Chemometric Analyses
- Remote-Sensing Image Classification
- Land Use and Ecosystem Services
- Urban Heat Island Mitigation
- Leaf Properties and Growth Measurement
- Remote Sensing and Land Use
- Species Distribution and Climate Change
- Genetic Mapping and Diversity in Plants and Animals
- Greenhouse Technology and Climate Control
- Mycotoxins in Agriculture and Food
- 3D Surveying and Cultural Heritage
- Soil Geostatistics and Mapping
- Plant responses to elevated CO2
- Water Quality Monitoring Technologies
- Horticultural and Viticultural Research
- African Botany and Ecology Studies
- Identification and Quantification in Food
- Seed and Plant Biochemistry
- Urban Green Space and Health
- Image Enhancement Techniques
- Precipitation Measurement and Analysis
- Plant Pathogens and Fungal Diseases
South Dakota State University
2022-2025
Saint Louis University
2014-2021
Xinjiang Agricultural University
2010-2015
Non-destructive crop monitoring over large areas with high efficiency is of great significance in precision agriculture and plant phenotyping, as well decision making regards to grain policy food security. The goal this research was assess the potential combining canopy spectral information structure features for using satellite/unmanned aerial vehicle (UAV) data fusion machine learning. Worldview-2/3 satellite were tasked synchronized high-resolution RGB image collection an inexpensive...
The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with decreased cost and greater accessibility both UAVs thermal imaging sensors, has led to the widespread use this technology, especially for precision agriculture plant phenotyping. There are several camera systems market that available at a low cost. However, their efficacy accuracy various applications not been tested. In study, three commercially UAV cameras, including ICI 8640 P-series (Infrared Cameras...
Urban areas feature complex and heterogeneous land covers which create challenging issues for tree species classification. The increased availability of high spatial resolution multispectral satellite imagery LiDAR datasets combined with the recent evolution deep learning within remote sensing object detection scene classification, provide promising opportunities to map individual greater accuracy resolution. However, there are knowledge gaps that related contribution Worldview-3 SWIR bands,...
Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading entire vineyard. Hyperspectral remote sensing can potentially detect and quantify a nondestructive manner. This study utilized hyperspectral imagery at plant level identify classify grapevines inoculated with newly discovered DNA virus vein-clearing (GVCV) asymptomatic stages. An experiment was set up test site South Farm Research Center, Columbia, MO, USA (38.92 N,...
Urban tree species classification is a challenging task due to spectral and spatial diversity within an urban environment. Unmanned aerial vehicle (UAV) platforms small-sensor technology are rapidly evolving, presenting the opportunity for comprehensive multi-sensor remote sensing approach classification. The objectives of this paper were develop data fusion technique with limited training samples. To that end, UAV-based multispectral, hyperspectral, LiDAR, thermal infrared imagery was...
Current strategies for phenotyping above-ground biomass in field breeding nurseries demand significant investment both time and labor. Unmanned aerial vehicles (UAV) can be used to derive vegetation indices (VIs) with high throughput could provide an efficient way predict forage yield accuracy. The main objective of the study is investigate potential UAV-based multispectral data machine learning approaches estimation oat biomass. UAV equipped a sensor was flown over three experimental fields...
Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately non-destructively. This study utilized leaf-level hyperspectral data with derivative calculus machine learning sorghum. We calculated fractional (FD) orders starting from 0.2 2.0 order increments. Additionally, 43 common vegetation indices (VIs) were...
Abstract. Early stress detection is critical for proactive field management and terminal yield prediction, can aid policy making improved food security in the context of climate change population growth. Field surveys crop monitoring are destructive, labor-intensive, time-consuming not ideal large-scale spatial temporal monitoring. Recent technological advances Unmanned Aerial Vehicle (UAV) high-resolution satellite imaging with frequent revisit time have proliferated applications this...
Crop yield prediction before the harvest is crucial for food security, grain trade, and policy making. Previously, several machine learning methods have been applied to predict crop using different types of variables. In this study, we propose Geographically Weighted Random Forest Regression (GWRFR) approach improve at county level in US Corn Belt. We trained GWRFR five other popular algorithms (Multiple Linear (MLR), Partial Least Square (PLSR), Support Vector (SVR), Decision Tree (DTR),...
Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding climate-resilient wheat cultivars. In this study, we explored applicability Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for or multi-trait (MT) prediction grain yield (GY), test weight (TW), protein content (GPC) in winter wheat. Significant correlations were observed between agronomic HTP-based across different...
The recently developed OPtical TRapezoid Model (OPTRAM) has been successfully applied for watershed scale soil moisture (SM) estimation based on remotely-sensed shortwave infrared (SWIR) transformed reflectance (TRSWIR) and the normalized difference vegetation index (NDVI). This study is aimed at evaluation of OPTRAM field precision agriculture applications using ultrahigh spatial resolution optical observations obtained with one world’s largest robotic phenotyping scanners located in...
Near-earth hyperspectral big data present both huge opportunities and challenges for spurring developments in agriculture high-throughput plant phenotyping breeding. In this article, we data-driven approaches to address the calibration utilizing near-earth agriculture. A data-driven, fully automated workflow that includes a suite of robust algorithms radiometric calibration, bidirectional reflectance distribution function (BRDF) correction normalization, soil shadow masking, image quality...
Achieving global sustainable agriculture requires farmers worldwide to adopt smart agricultural technologies, such as autonomous ground robots. However, most robots are either task- or crop-specific and expensive for small-scale smallholders. Therefore, there is a need cost-effective robotic platforms that modular by design can be easily adapted varying tasks crops. This paper describes the hardware of unique, low-cost multiaxial robot (ModagRobot), its field evaluation soybean phenotyping....
Efficient and accurate methods to monitor crop physiological responses help growers better understand physiology improve productivity. In recent years, developments in unmanned aerial vehicles (UAV) sensor technology have enabled image acquisition at very-high spectral, spatial, temporal resolutions. However, potential applications limitations of very-high-resolution (VHR) hyperspectral thermal UAV imaging for characterization plant diurnal remain largely unknown, due issues related shadow...
The pre-harvest estimation of seed composition from standing crops is imperative for field management practices and plant phenotyping. This paper presents the first time potential Unmanned Aerial Vehicles (UAV)-based high-resolution hyperspectral LiDAR data acquired in-season stand estimating protein oil compositions soybean corn using multisensory fusion automated machine learning. UAV-based was collected during growing season (reproductive stage five (R5)) 2020 over a test site near...
Accurate and timely monitoring of biomass in breeding nurseries is essential for evaluating plant performance selecting superior genotypes. Traditional methods phenotyping above-ground field conditions requires significant time, cost, labor. Unmanned Aerial Vehicles (UAVs) offer a rapid non-destructive approach multiple plots at low cost. While Vegetation Indices (VIs) extracted from remote sensing imagery have been widely employed estimation, they mainly capture spectral information...