High throughput phenotyping of tomato spot wilt disease in peanuts using unmanned aerial systems and multispectral imaging

2017 ◽  
Vol 20 (3) ◽  
pp. 4-12 ◽  
Author(s):  
Aaron Patrick ◽  
Sara Pelham ◽  
Albert Culbreath ◽  
C. Corely Holbrook ◽  
Ignacio Jose De Godoy ◽  
...  
Keyword(s):  
High Throughput ◽  
Multispectral Imaging ◽  
Wilt Disease ◽  
Unmanned Aerial Systems ◽  
High Throughput Phenotyping ◽  
Aerial Systems

An Efficient Pipeline for Crop Image Extraction and Vegetation Index Derivation Using Unmanned Aerial Systems

2020 ◽  
Vol 63 (4) ◽  
pp. 1133-1146
Author(s):  
Beichen Lyu ◽  
Stuart D. Smith ◽  
Yexiang Xue ◽  
Katy M. Rainey ◽  
Keith Cherkauer
Keyword(s):  
Data Processing ◽  
High Throughput ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Unmanned Aerial Systems ◽  
Growth Stages ◽  
Processing Pipeline ◽  
High Throughput Phenotyping ◽  
Aerial Systems

HighlightsThis study addresses two computational challenges in high-throughput phenotyping: scalability and efficiency.Specifically, we focus on extracting crop images and deriving vegetation indices using unmanned aerial systems.To this end, we outline a data processing pipeline, featuring a crop localization algorithm and trie data structure.We demonstrate the efficacy of our approach by computing large-scale and high-precision vegetation indices in a soybean breeding experiment, where we evaluate soybean growth under water inundation and temporal change.Abstract. In agronomy, high-throughput phenotyping (HTP) can provide key information for agronomists in genomic selection as well as farmers in yield prediction. Recently, HTP using unmanned aerial systems (UAS) has shown advantages in both cost and efficiency. However, scalability and efficiency have not been well studied when processing images in complex contexts, such as using multispectral cameras, and when images are collected during early and late growth stages. These challenges hamper further analysis to quantify phenotypic traits for large-scale and high-precision applications in plant breeding. To solve these challenges, our research team previously built a three-step data processing pipeline, which is highly modular. For this project, we present improvements to the previous pipeline to improve canopy segmentation and crop plot localization, leading to improved accuracy in crop image extraction. Furthermore, we propose a novel workflow based on a trie data structure to compute vegetation indices efficiently and with greater flexibility. For each of our proposed changes, we evaluate the advantages by comparison with previous models in the literature or by comparing processing results using both the original and improved pipelines. The improved pipeline is implemented as two MATLAB programs: Crop Image Extraction version 2 (CIE 2.0) and Vegetation Index Derivation version 1 (VID 1.0). Using CIE 2.0 and VID 1.0, we compute canopy coverage and normalized difference vegetation indices (NDVIs) for a soybean phenotyping experiment. We use canopy coverage to investigate excess water stress and NDVIs to evaluate temporal patterns across the soybean growth stages. Both experimental results compare favorably with previous studies, especially for approximation of soybean reproductive stage. Overall, the proposed methodology and implemented experiments provide a scalable and efficient paradigm for applying HTP with UAS to general plant breeding. Keywords: Data processing pipeline, High-throughput phenotyping, Image processing, Soybean breeding, Unmanned aerial systems, Vegetation indices.

scholarly journals Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries

Plant Methods ◽  
2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Atena Haghighattalab ◽  
Lorena González Pérez ◽  
Suchismita Mondal ◽  
Daljit Singh ◽  
Dale Schinstock ◽  
...  
Keyword(s):  
High Throughput ◽  
Wheat Breeding ◽  
Unmanned Aerial Systems ◽  
High Throughput Phenotyping ◽  
Aerial Systems

scholarly journals Multispectral imaging and unmanned aerial systems for cotton plant phenotyping

PLoS ONE ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. e0205083 ◽  
Author(s):  
Rui Xu ◽  
Changying Li ◽  
Andrew H. Paterson
Keyword(s):  
Cotton Plant ◽  
Multispectral Imaging ◽  
Plant Phenotyping ◽  
Unmanned Aerial Systems ◽  
Aerial Systems

scholarly journals UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence

2019 ◽  
Vol 11 (4) ◽  
pp. 410 ◽  
Author(s):  
Yiannis Ampatzidis ◽  
Victor Partel
Keyword(s):  
Artificial Intelligence ◽  
Data Acquisition ◽  
High Throughput ◽  
Multispectral Imaging ◽  
Phenotypic Trait ◽  
Phenotypic Characteristics ◽  
Individual Tree ◽  
High Throughput Phenotyping ◽  
Citrus Varieties ◽  
Canopy Size

Traditional plant breeding evaluation methods are time-consuming, labor-intensive, and costly. Accurate and rapid phenotypic trait data acquisition and analysis can improve genomic selection and accelerate cultivar development. In this work, a technique for data acquisition and image processing was developed utilizing small unmanned aerial vehicles (UAVs), multispectral imaging, and deep learning convolutional neural networks to evaluate phenotypic characteristics on citrus crops. This low-cost and automated high-throughput phenotyping technique utilizes artificial intelligence (AI) and machine learning (ML) to: (i) detect, count, and geolocate trees and tree gaps; (ii) categorize trees based on their canopy size; (iii) develop individual tree health indices; and (iv) evaluate citrus varieties and rootstocks. The proposed remote sensing technique was able to detect and count citrus trees in a grove of 4,931 trees, with precision and recall of 99.9% and 99.7%, respectively, estimate their canopy size with overall accuracy of 85.5%, and detect, count, and geolocate tree gaps with a precision and recall of 100% and 94.6%, respectively. This UAV-based technique provides a consistent, more direct, cost-effective, and rapid method to evaluate phenotypic characteristics of citrus varieties and rootstocks.

scholarly journals Performance of aerial Bacillus thuringiensis var. israelensis applications in mixed saltmarsh-mangrove systems and use of affordable unmanned aerial systems to identify problematic levels of canopy cover

2020 ◽  
Author(s):  
Brian J. Johnson ◽  
Russell Manby ◽  
Gregor J. Devine
Keyword(s):  
Bacillus Thuringiensis ◽  
Multispectral Imaging ◽  
Mosquito Control ◽  
Larval Mortality ◽  
Canopy Cover ◽  
Ground Level ◽  
Unmanned Aerial Systems ◽  
Heterogeneous Environments ◽  
Vegetation Class ◽  
Aerial Systems

ABSTRACTBACKGROUNDIn the Australian southeast, the saltmarsh mosquito Aedes vigilax (Skuse) is the focus of area-wide larviciding campaigns employing the biological agent Bacillus thuringiensis var. israelensis (Bti). Although generally effective, frequent inundating tides and considerable mangrove cover can make control challenging. Here, we describe the efficacy and persistence of an aqueous Bti suspension (potency: 1200 International Toxic Units; strain AM65-52) within a mixed saltmarsh-mangrove system and the use of affordable unmanned aerial systems (UAS) to identify and map problematic levels of mangrove canopy cover.RESULTSHigh mangrove canopy density (>40% cover) reduced product deposition by 74.5% (0.013± 0.002 μl/cm2 vs. 0.051± 0.006 μl/cm2), larval mortality by 27.7% (60.7± 4.1% vs. 84.0± 2.4%), and ground level Bti concentrations by 32.03% (1144 ± 462.6 vs. 1683 ± 447.8 spores ml−1) relative to open saltmarsh. Persistence of product post-application was found to be low (80.6% loss at 6 h) resulting in negligible additional losses to tidal inundation 24 h post-application. UAS surveys accurately identified areas of high mangrove cover using both standard and multispectral imagery, although derived index values for this vegetation class were only moderately correlated with ground measurements (R2=0.17-0.38) at their most informative scales.CONCLUSIONThese findings highlight the complex operational challenges that affect coastal mosquito control in heterogeneous environments. The problem is exacerbated by continued mangrove transgression into saltmarsh habitat in the region. Emerging UAS technology can help operators optimize treatments by accurately identifying and mapping challenging canopy cover using both standard and multispectral imaging.

scholarly journals Quantifying Effects of Excess Water Stress at Early Soybean Growth Stages Using Unmanned Aerial Systems

2021 ◽  
Vol 13 (15) ◽  
pp. 2911
Author(s):  
Stuart D. Smith ◽  
Laura C. Bowling ◽  
Katy M. Rainey ◽  
Keith A. Cherkauer
Keyword(s):  
Water Stress ◽  
High Throughput ◽  
Low Cost ◽  
Unmanned Aerial Systems ◽  
Above Ground Biomass ◽  
Development Stages ◽  
High Throughput Data ◽  
Processing Pipeline ◽  
Excess Water ◽  
Aerial Systems

Low-gradient agricultural areas prone to in-field flooding impact crop development and yield potential, resulting in financial losses. Early identification of the potential reduction in yield from excess water stress at the plot scale provides stakeholders with the high-throughput information needed to assess risk and make responsive economic management decisions as well as future investments. The objective of this study is to analyze and evaluate the application of proximal remote sensing from unmanned aerial systems (UAS) to detect excess water stress in soybean and predict the potential reduction in yield due to this excess water stress. A high-throughput data processing pipeline is developed to analyze multispectral images captured at the early development stages (R4–R5) from a low-cost UAS over two radiation use efficiency experiments in West–Central Indiana, USA. Above-ground biomass is estimated remotely to assess the soybean development by considering soybean genotype classes (High Yielding, High Yielding under Drought, Diversity, all classes) and transferring estimated parameters to a replicate experiment. Digital terrain analysis using the Topographic Wetness Index (TWI) is used to objectively compare plots more susceptible to inundation with replicate plots less susceptible to inundation. The results of the study indicate that proximal remote sensing estimates above-ground biomass at the R4–R5 stage using adaptable and transferable methods, with a calculated percent bias between 0.8% and 14% and root mean square error between 72 g/m2 and 77 g/m2 across all genetic classes. The estimated biomass is sensitive to excess water stress with distinguishable differences identified between the R4 and R5 development stages; this translates into a reduction in the percent of expected yield corresponding with observations of in-field flooding and high TWI. This study demonstrates transferable methods to estimate yield loss due to excess water stress at the plot level and increased potential to provide crop status assessments to stakeholders prior to harvest using low-cost UAS and a high-throughput data processing pipeline.

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