☆ 4.5 Review

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

CROP SCIENCE (2023)

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Article Genetics & Heredity

Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions

Alper Adak et al.

Summary: A major challenge in genetic improvement and selection is accurately predicting individuals with the highest fitness without direct measurement. Genomic predictions (GP) based on genome-wide markers have become reliable, but now the use of phenotyping technologies, such as drones, can achieve similar or even better prediction power than genomic data. This study compared drone data and genomic data in predicting maize hybrids, and found that temporal phenomic prediction (TPP) outperformed GP in cross-validation. TPP also showed successful integration of small effect loci and worked well on unrelated individuals.

G3-GENES GENOMES GENETICS (2023)

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Article Chemistry, Analytical

Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning

Fengkai Tian et al.

Summary: Weeds are a major issue for agricultural production and will continue to cause yield losses due to climate change. The use of dicamba to control weeds in genetically engineered dicot crops has resulted in off-target damage and yield losses for non-tolerant crops. This study explores the use of unmanned aerial vehicle imagery and deep learning to assess off-target dicamba damage in soybean genotypes, showing promising results for improving breeding efficiency.

SENSORS (2023)

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Article Agronomy

Utilizing Genomic Selection for Wheat Population Development and Improvement

Lance F. Merrick et al.

Summary: This article discusses the integration of genomic selection (GS) into wheat breeding programs to increase genetic gain and reduce the time required to release new varieties. By redesigning the breeding pipeline and implementing a two-part breeding strategy, the potential of GS to change wheat breeding is explored.

AGRONOMY-BASEL (2022)

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Review Agriculture, Multidisciplinary

Fruit yield prediction and estimation in orchards: A state-of-the-art comprehensive review for both direct and indirect methods

Leilei He et al.

Summary: Orchard pre-harvest yield data is important for fruit growers, and the traditional manual operation for estimation is burdensome and time-consuming. Automatic yield monitoring through comprehensive prediction systems or intelligent equipment is the main approach. Machine learning is the most commonly used modeling method, and machine vision systems based on image processing and deep learning have been rapidly developed and widely applied in orchard yield prediction.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2022)

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Article Environmental Sciences

Optimization of UAV-Based Imaging and Image Processing Orthomosaic and Point Cloud Approaches for Estimating Biomass in a Forage Crop

Worasit Sangjan et al.

Summary: This study utilized UAV equipped with RGB and multispectral cameras to estimate above-ground biomass (AGBM) of winter pea in breeding trials. Results showed that 3D model developed from UAV-based RGB imagery provided consistent and high correlation with fresh and dry AGBM, demonstrating potential for estimating crop AGBM in winter pea breeding trials.

REMOTE SENSING (2022)

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Article Environmental Sciences

Hyperspectral UAV Images at Different Altitudes for Monitoring the Leaf Nitrogen Content in Cotton Crops

Caixia Yin et al.

Summary: This study tested the feasibility of using a UAV equipped with a hyperspectral spectrometer to monitor cotton leaf nitrogen content. The results showed that collecting UAV hyperspectral images at multiple heights improved the accuracy of assessing cotton nitrogen content.

REMOTE SENSING (2022)

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Article Plant Sciences

Cotton Yield Estimation From Aerial Imagery Using Machine Learning Approaches

Javier Rodriguez-Sanchez et al.

Summary: The study aims to predict cotton yield in breeding fields using machine learning techniques and aerial imagery. By training a Support Vector Machine classifier to identify cotton pixels and clustering them, the study successfully predicts the number of cotton bolls at the plot level. The results demonstrate that aerial imagery with machine learning can be a reliable, efficient, and effective tool for pre-harvest cotton yield prediction.

FRONTIERS IN PLANT SCIENCE (2022)

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Article Environmental Sciences

Predictive Modeling of Above-Ground Biomass in Brachiaria Pastures from Satellite and UAV Imagery Using Machine Learning Approaches

Cesar Alvarez-Mendoza et al.

Summary: This study demonstrates the potential of machine learning models to predict the quality and quantity of cattle feed based on above-ground canopy features in Brachiaria pasture at different scales. Different machine learning algorithms showed varying performance in predicting above-ground biomass, with some algorithms showing promise for large-scale satellite data while others excelling in smaller-scale UAV imagery. The findings provide valuable insights for pasture management in Colombia.

REMOTE SENSING (2022)

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Article Plant Sciences

Research on Field Soybean Weed Identification Based on an Improved UNet Model Combined With a Channel Attention Mechanism

Helong Yu et al.

Summary: This paper proposes an improved semantic segmentation method for addressing the problem of distinguishing different types of weeds in complex field environments. By modifying the UNet model structure and embedding a channel attention mechanism, the semantic gap is effectively eliminated, and high-quality weed segmentation images are obtained through digital image processing. Experimental results demonstrate that this method performs the best in complex field environments.

FRONTIERS IN PLANT SCIENCE (2022)

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Article Agronomy

Prediction of regrowth and biomass of perennial sorghum using unoccupied aerial systems

Shakirah Nakasagga et al.

Summary: This study used unmanned aerial systems to evaluate biomass yield and rhizome characteristics of perennial grain sorghum and found that the blue chromatic coordinate index (BCC) best predicted the measured traits. The study also showed that optimizing flights to specific dates after planting could minimize resource requirements and costs.

CROP SCIENCE (2022)

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Article Agronomy

Use of open-source object detection algorithms to detect Palmer amaranth (Amaranthus palmeri) in soybean

Isaac H. Barnhart et al.

Summary: This study investigated the use of open-source object detection algorithms to accurately detect Palmer amaranth weeds in soybean cropping systems. The results showed that the YOLOv5 algorithm achieved the highest mean average precision score and demonstrated potential for accurate weed detection in crops.

WEED SCIENCE (2022)

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Article Plant Sciences

Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean

Ashlyn Rairdin et al.

Summary: This research used deep learning techniques to analyze the GWAS of sudden death syndrome (SDS) in soybean. Disease severity was evaluated using both visual field ratings and DL-based severity ratings from images, and significant SNP markers associated with disease resistance were identified in both types of ratings.

FRONTIERS IN PLANT SCIENCE (2022)

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Article Robotics

Maize Tassel Detection From UAV Imagery Using Deep Learning

Aziza Alzadjali et al.

Summary: The timing of flowering is crucial for agricultural crop productivity, with both early and late flowering affecting growth and harvest. This study developed and compared two automatic tassel detection methods using deep learning models on imagery from unmanned aerial vehicles, achieving promising accuracy levels. Further investigation into CNN-based model structures could improve accuracy, speed, and generalizability for aerial-based tassel detection in the future.

FRONTIERS IN ROBOTICS AND AI (2021)

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Review Plant Sciences

Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping

Arti Singh et al.

Summary: Plant stress phenotyping is crucial for selecting stress-resistant varieties and improving stress-management strategies. Standardization of visual assessments and deployment of imaging techniques have enhanced accuracy and reliability of stress assessment, while machine learning methods are increasingly utilized to extract insights from high-dimensional datasets. A comprehensive strategy is proposed for utilizing machine learning techniques in applying plant stress phenotyping across various crops, stresses, and environments.

TRENDS IN PLANT SCIENCE (2021)

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Review Remote Sensing

A review on deep learning in UAV remote sensing

Lucas Prado Osco et al.

Summary: This paper reviews the recent research on DNN algorithms applied in UAV-based image applications, focusing on the use of classification and regression techniques and discussing potential future directions in this field.

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2021)

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Article Agriculture, Multidisciplinary

Maize and soybean heights estimation from unmanned aerial vehicle (UAV) LiDAR data

Shezhou Luo et al.

Summary: This study demonstrates that UAV LiDAR data can successfully estimate maize and soybean heights, with the method based on LiDAR variables producing more accurate estimates than the CHM-based method. Furthermore, the influence of LiDAR point density on crop height estimation precision was found to depend on the specific density values, with higher densities not necessarily leading to higher precision.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

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Article Geography, Physical

UAV-Thermal imaging and agglomerative hierarchical clustering techniques to evaluate and rank physiological performance of wheat genotypes on sodic soil

Sumanta Das et al.

Summary: The study utilized UAV-thermal imaging and agglomerative hierarchical clustering techniques to evaluate and rank physiological performance of different wheat genotypes grown in varying sodic soils. Results indicate that sodic soil constraints intensify plant water stress, and UAV-thermal imaging combined with AHC techniques can help identify cultivars tolerant to sodicity.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2021)

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Article Plant Sciences

Combining Genomic and Phenomic Information for Predicting Grain Protein Content and Grain Yield in Spring Wheat

Karansher S. Sandhu et al.

Summary: Genomics and high throughput phenomics have the potential to revolutionize wheat breeding. This study compared single and multi-trait GS models for predicting GPC and grain yield in wheat, showing that including secondary traits in the models increased accuracy. Spectral information collected at heading was superior for predicting GPC, while grain yield was more accurately predicted during the grain filling stage.

FRONTIERS IN PLANT SCIENCE (2021)

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Article Plant Sciences

High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation

Leonardo Volpato et al.

Summary: This study evaluates PH estimation using UAV-based high-resolution imagery and 3D CSM at different phenological stages in wheat inbred lines. Results show that UAV-based PH estimates provide similar and higher heritability across different growth stages and years.

FRONTIERS IN PLANT SCIENCE (2021)

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Article Plant Sciences

Genomic prediction modeling of soybean biomass using UAV-based remote sensing and longitudinal model parameters

Yusuke Toda et al.

Summary: This study focused on using growth models with a few parameters to analyze remote sensing data and investigated if a multitrait GP model using these parameters could improve soybean biomass prediction accuracy.

PLANT GENOME (2021)

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Article Environmental Sciences

Deep Learning-Based Object Detection System for Identifying Weeds Using UAS Imagery

Aaron Etienne et al.

Summary: This research aimed to develop accurate weed detection system in field conditions by creating a large annotated database of RGB images and training deep learning models. The best performance was observed with images acquired at 10 meters above ground level, achieving high average precision scores for monocot and dicot weeds.

REMOTE SENSING (2021)

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Article Agriculture, Multidisciplinary

UAV-based high-throughput phenotyping to increase prediction and selection accuracy in maize varieties under artificial MSV inoculation

Walter Chivasa et al.

Summary: The study demonstrates that the use of UAV-based multispectral remote sensing is a reliable tool for phenotyping MSV disease and grain yield prediction. The mid-vegetative stage appears to be the most ideal phenological stage for predicting MSV and grain yield.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

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Article Genetics & Heredity

Validation of functional polymorphisms affecting maize plant height by unoccupied aerial systems discovers novel temporal phenotypes

Alper Adak et al.

Summary: Genetic and phenotypic studies have shown that the plant height in maize is determined by numerous quantitative trait loci, with variable effects observed across different genetic backgrounds. The dynamic allelic effects of these economically valuable loci present opportunities for breeding and selection to expand the genetic basis of maize.

G3-GENES GENOMES GENETICS (2021)

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Article Plant Sciences

Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize

Alper Adak et al.

Summary: Traditional phenotyping methods, combined with genetic mapping, have identified loci governing complex traits in crops. UAS-based phenotyping revealed the dynamic relationship between time-specific loci and complex traits. The study showed the importance of plant height in predicting maize grain yield under different management conditions, as well as the role of candidate genes in regulating plant architecture during early maize growth.

PLANT GENOME (2021)

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Article Computer Science, Information Systems

Detection and Analysis of Degree of Maize Lodging Using UAV-RGB Image Multi-Feature Factors and Various Classification Methods

Zixu Wang et al.

Summary: A method was developed to accurately determine the degree of crop lodging by fusing supervised and object-oriented classifications based on spectrum, texture, and canopy structure data. Factors affecting lodging degree were analyzed based on spatial distribution, enabling determination of optimal sowing date, density, and fertilization method.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2021)

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Article Agronomy

Comparative prediction accuracy of hyperspectral bands for different soybean crop variables: From leaf area to seed composition

Mariana Chiozza et al.

Summary: The study aimed to rank the prediction accuracy among different crop variables using hyperspectral bands captured at different timepoints during the growing season. Results showed that LAI can be best predicted using reflectance information, and suggest that hyperspectral bands are necessary but not sufficient to improve the prediction of other crop variables such as biomass, seed yield, and seed composition traits.

FIELD CROPS RESEARCH (2021)

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Article Chemistry, Analytical

Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing

Gabriel Silva de Oliveira et al.

Summary: This study aimed to propose a convolutional neural network (CNN) approach using UAV-RGB imagery to estimate dry matter yield traits in a guineagrass breeding program. The analysis of different CNN architectures showed that the pretrained ResNeXt50 model achieved the best results for indirect selection, demonstrating promising potential for high-throughput phenotyping in forage breeding programs.

SENSORS (2021)

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Article Environmental Sciences

Temporal Vegetation Indices and Plant Height from Remotely Sensed Imagery Can Predict Grain Yield and Flowering Time Breeding Value in Maize via Machine Learning Regression

Alper Adak et al.

Summary: Utilizing UAS imagery, this study successfully predicted grain yield and flowering time in elite hybrid maize breeding trials, demonstrating significant improvements in prediction accuracy and reduction in error compared to traditional linear regression methods. Machine learning-based regressions, particularly ridge regression, showed promise in predicting these key traits.

REMOTE SENSING (2021)

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Article Environmental Sciences

Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding

Paul Herzig et al.

Summary: With advances in plant genomics, plant phenotyping is a new bottleneck in plant breeding, leading to the need for reliable high-throughput plant phenotyping techniques. New sensor-based high-throughput phenotyping techniques are increasingly being used in plant breeding research to provide non-destructive, objective, and continuous plant characterization for yield prediction. Comparison of two sensor systems attached to unmanned aerial vehicles shows that RGB imagery is more suitable for yield prediction due to lower costs and user-friendly handling.

REMOTE SENSING (2021)

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Article Environmental Sciences

Machine Learning Techniques to Predict Soybean Plant Density Using UAV and Satellite-Based Remote Sensing

Luthfan Nur Habibi et al.

Summary: The study aimed to develop a plant density measurement model using data sets of different spatial resolutions, including UAV and satellite imagery. Results showed that the YOLOv3 model accurately measured soybean plant density from UAV imagery, and regression models using PlanetScope and climate data expanded the prediction areas. The established model demonstrated acceptable prediction accuracy for estimating plant density.

REMOTE SENSING (2021)

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Article Environmental Sciences

Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture

Rui Xu et al.

Summary: An unmanned aerial system with integrated multi-sensors was developed for plant phenotyping, demonstrating potential value in plant breeding and precision crop management through validation with ground data collected in a cotton field.

REMOTE SENSING (2021)

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Article Environmental Sciences

Effect of Atmospheric Corrections on NDVI: Intercomparability of Landsat 8, Sentinel-2, and UAV Sensors

David Moravec et al.

Summary: This study tested the impact of six different atmospheric correction methods on NDVI results, finding significant differences in results based on the method chosen. It also demonstrated that proper use of atmospheric correction methods can increase the intercomparability between data from Landsat 8 and Sentinel-2 satellite imagery.

REMOTE SENSING (2021)

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Article Agronomy

Qualification of Soybean Responses to Flooding Stress Using UAV-Based Imagery and Deep Learning

Jing Zhou et al.

Summary: This study aimed to investigate the potential of estimating flood-induced soybean injuries using UAV-based image features collected at different flight heights. Results show that image features were significantly different at three flight heights, with the best classification performance achieved using features extracted at 20 meters with a 0.9 accuracy for the five-level flooding injury score (FIS).

PLANT PHENOMICS (2021)

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Article Agronomy

Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications

Luis G. Riera et al.

Summary: This study aims to predict soybean seed yield rank from in-field video data using a machine learning approach, showing promise in reducing time and human effort for breeding decisions. The use of multiview image fusion and deep learning framework demonstrates the effectiveness of this method in seed yield estimation.

PLANT PHENOMICS (2021)

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Review Agronomy

UAS-Based Plant Phenotyping for Research and Breeding Applications

Wei Guo et al.

Summary: Unmanned aircraft systems (UAS) are a powerful tool for plant phenotyping, with advantages in cost, ease of use, sensor payload diversity, and connectivity. This paper reviews the current status of data deployment, collection, and analysis from UAS-based phenotyping platforms, discussing technical challenges and future trends. The review aims to provide insights for plant science practitioners interested in utilizing UAS-based imaging for phenotyping in research and breeding.

PLANT PHENOMICS (2021)

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Article Remote Sensing

Effect of the Solar Zenith Angles at Different Latitudes on Estimated Crop Vegetation Indices

Milton Valencia-Ortiz et al.

Summary: The study evaluated the variation in standard vegetation indices and their relationship with ground-reference data across two different latitudes using UAV-based multispectral images. The simple ratio index (SR) showed less variability across solar zenith angles in both latitude zones, while other vegetation indices were affected by SZA. The correlation between vegetation indices and ground-reference data remained stable across different solar zenith angles in both latitude zones.

DRONES (2021)

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Article Remote Sensing

UAV-based thermal imaging in the assessment of water status of soybean plants

Luis Guilherme Teixeira Crusiol et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2020)

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Article Entomology

Detection of Stress Induced by Soybean Aphid (Hemiptera: Aphididae) Using Multispectral Imagery from Unmanned Aerial Vehicles

Zachary P. D. Marston et al.

JOURNAL OF ECONOMIC ENTOMOLOGY (2020)

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Article Biochemistry & Molecular Biology

Genomic Prediction and Indirect Selection for Grain Yield in US Pacific Northwest Winter Wheat Using Spectral Reflectance Indices from High-Throughput Phenotyping

Dennis N. Lozada et al.

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2020)

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Review Biochemistry & Molecular Biology

Breeder friendly phenotyping

Matthew Reynolds et al.

PLANT SCIENCE (2020)

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Article Chemistry, Analytical

High Throughput Phenotyping for Various Traits on Soybean Seeds Using Image Analysis

JeongHo Baek et al.

SENSORS (2020)

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Article Agricultural Engineering

Yield estimation in cotton using UAV-based multi-sensor imagery

Aijing Feng et al.

BIOSYSTEMS ENGINEERING (2020)

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Article Geochemistry & Geophysics

Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks

Everton Castelao Tetila et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2020)

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Article Multidisciplinary Sciences

Gains through selection for grain yield in a winter wheat breeding program

Dennis N. Lozada et al.

PLOS ONE (2020)

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Article Environmental Sciences

Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning

Maitiniyazi Maimaitijiang et al.

REMOTE SENSING (2020)

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Article Environmental Sciences

Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials

Irene Borra-Serrano et al.

REMOTE SENSING (2020)

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Review Engineering, Multidisciplinary

Remote Sensing and Precision Agriculture Technologies for Crop Disease Detection and Management with a Practical Application Example

Chenghai Yang

ENGINEERING (2020)

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Article Agriculture, Multidisciplinary

Classification of soybean leaf wilting due to drought stress using UAV-based imagery

Jing Zhou et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

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Article Agronomy

Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data

Longfei Zhou et al.

AGRICULTURE-BASEL (2020)

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Article Plant Sciences

Unoccupied aerial system enabled functional modeling of maize height reveals dynamic expression of loci

Steven L. I. I. I. I. Anderson et al.

PLANT DIRECT (2020)

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Article Plant Sciences

UAV-based imaging platform for monitoring maize growth throughout development

Sara B. Tirado et al.

PLANT DIRECT (2020)

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Article Biotechnology & Applied Microbiology

Phenomics-based GWAS analysis reveals the genetic architecture for drought resistance in cotton

Baoqi Li et al.

PLANT BIOTECHNOLOGY JOURNAL (2020)

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Article Environmental Sciences

Automatic Detection of Maize Tassels from UAV Images by Combining Random Forest Classifier and VGG16

Xuli Zan et al.

REMOTE SENSING (2020)

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Review Environmental Sciences

Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models

Roghieh Eskandari et al.

REMOTE SENSING (2020)

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Article Plant Sciences

R/UAStools::plotshpcreate: Create Multi-Polygon Shapefiles for Extraction of Research Plot Scale Agriculture Remote Sensing Data

Steven L. Anderson et al.

FRONTIERS IN PLANT SCIENCE (2020)

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Article Agronomy

Canopy Roughness: A New Phenotypic Trait to Estimate Aboveground Biomass from Unmanned Aerial System

Monica Herrero-Huerta et al.

PLANT PHENOMICS (2020)

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Review Agriculture, Multidisciplinary

A review on plant high-throughput phenotyping traits using UAV-based sensors

Chuanqi Xie et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

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Article Environmental Sciences

Monitoring Wheat Leaf Rust and Stripe Rust in Winter Wheat Using High-Resolution UAV-Based Red-Green-Blue Imagery

Ramin Heidarian Dehkordi et al.

REMOTE SENSING (2020)

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Proceedings Paper Automation & Control Systems

An open, scalable, and flexible framework for automated aerial measurement of field experiments

Christophe Schnaufer et al.

AUTONOMOUS AIR AND GROUND SENSING SYSTEMS FOR AGRICULTURAL OPTIMIZATION AND PHENOTYPING V (2020)

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Article Environmental Sciences

High-Throughput Phenotyping of Soybean Maturity Using Time Series UAV Imagery and Convolutional Neural Networks

Rodrigo Trevisan et al.

REMOTE SENSING (2020)

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Article Geography, Physical

Developing a machine learning based cotton yield estimation framework using multi-temporal UAS data

Akash Ashapure et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2020)

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Article Multidisciplinary Sciences

Multispectral imaging and unmanned aerial systems for cotton plant phenotyping

Rui Xu et al.

PLOS ONE (2019)

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Article Genetics & Heredity

Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

Margaret R. Krause et al.

G3-GENES GENOMES GENETICS (2019)

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Article Genetics & Heredity

New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes

Osval A. Montesinos-Lopez et al.

G3-GENES GENOMES GENETICS (2019)

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Article Geography, Physical

Vegetation Index Weighted Canopy Volume Model (CVMVI) for soybean biomass estimation from Unmanned Aerial System-based RGB imagery

Maitiniyazi Maimaitijiang et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2019)

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Article Plant Sciences

High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat

Daljit Singh et al.

FRONTIERS IN PLANT SCIENCE (2019)

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Article Geography, Physical

A novel framework to detect conventional tillage and no-tillage cropping system effect on cotton growth and development using multi-temporal UAS data

Akash Ashapure et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2019)

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Article Environmental Sciences

Estimation of the Maturity Date of Soybean Breeding Lines Using UAV-Based Multispectral Imagery

Jing Zhou et al.

REMOTE SENSING (2019)

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Article Plant Sciences

Improving and Maintaining Winter Hardiness and Frost Tolerance in Bread Wheat by Genomic Selection

Sebastian Michel et al.

FRONTIERS IN PLANT SCIENCE (2019)

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Article Agriculture, Multidisciplinary

Unsupervised deep learning and semi-automatic data labeling in weed discrimination

Alessandro dos Santos Ferreira et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)

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Article Chemistry, Analytical

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Shaolong Zhu et al.

SENSORS (2019)

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Article Environmental Sciences

Radiometric and Atmospheric Corrections of Multispectral μMCA Camera for UAV Spectroscopy

Robert Minarik et al.

REMOTE SENSING (2019)

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Article Environmental Sciences

A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data

Akash Ashapure et al.

REMOTE SENSING (2019)

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Article Plant Sciences

Helios: A Scalable 3D Plant and Environmental Biophysical Modeling Framework

Brian N. Bailey

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Article Agriculture, Multidisciplinary

Assessment of sudden death syndrome in soybean through multispectral broadband remote sensing aboard small unmanned aerial systems

Nicholle M. Hatton et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)

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Article Biochemistry & Molecular Biology

A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform

Muhammad Adeel Hassan et al.

PLANT SCIENCE (2019)

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Review Plant Sciences

Plant Phenotyping Research Trends, a Science Mapping Approach

Corrado Costa et al.

FRONTIERS IN PLANT SCIENCE (2019)

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Article Agronomy

Estimating Crop Transpiration of Soybean under Different Irrigation Treatments Using Thermal Infrared Remote Sensing Imagery

Mengjie Hou et al.

AGRONOMY-BASEL (2019)

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Proceedings Paper Automation & Control Systems

Improving Soybean Breeding Using UAV Measurements of Physiological Maturity

B. Narayanan et al.

AUTONOMOUS AIR AND GROUND SENSING SYSTEMS FOR AGRICULTURAL OPTIMIZATION AND PHENOTYPING IV (2019)

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Article Engineering, Electrical & Electronic

A Subband Radiometric Calibration Method for UAV-Based Multispectral Remote Sensing

Lei Deng et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2018)

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Article Remote Sensing

Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery

L. Malambo et al.

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2018)

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Article Agronomy

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