scholarly journals Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Michael Gomez Selvaraj ◽  
Manuel Valderrama ◽  
Diego Guzman ◽  
Milton Valencia ◽  
Henry Ruiz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
High Throughput ◽  
Manihot Esculenta ◽  
Manihot Esculenta Crantz ◽  
Field Phenotyping ◽  
Below Ground ◽  
Insight Into

scholarly journals Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)

2020 ◽  
Author(s):  
Michael Gomez Selvaraj ◽  
Manuel Valderrama ◽  
Diego Guzman ◽  
Milton Valencia ◽  
Henry Ruiz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Vegetation Indices ◽  
Analytical Framework ◽  
Aerial Image ◽  
Root Yield ◽  
Breeding Programs ◽  
Cassava Root ◽  
Below Ground

Abstract Background: Rapid non-destructive measurements to predict cassava root yield over the full growing season through large numbers of germplasm and multiple environments is a huge challenge in Cassava breeding programs. As opposed to waiting until the harvest season, multispectral imagery using unmanned aerial vehicles (UAV) are capable of measuring the canopy metrics and vegetation indices (VIs) traits at different time points of the growth cycle. This resourceful time series aerial image processing with appropriate analytical framework is very important for the automatic extraction of phenotypic features from the image data. Many studies have demonstrated the usefulness of advanced remote sensing technologies coupled with machine learning (ML) approaches for accurate prediction of valuable crop traits. Until now, Cassava has received little to no attention in aerial image-based phenotyping and ML model testing. Results: To accelerate image processing, an automated image-analysis framework called CIAT Pheno-i was developed to extract plot level vegetation indices/canopy metrics. Multiple linear regression models were constructed at different key growth stages of cassava, using ground-truth data and vegetation indices obtained from a multispectral sensor. Henceforth, the spectral indices/features were combined to develop models and predict cassava root yield using different Machine learning techniques. Our results showed that (1) Developed CIAT pheno-i image analysis framework was found to be easier and more rapid than manual methods. (2) The correlation analysis of four phenological stages of cassava revealed that elongation (EL) and late bulking (LBK) were the most useful stages to estimate above-ground biomass (AGB), below-ground biomass (BGB) and canopy height (CH). (3) The multi-temporal analysis revealed that cumulative image feature information of EL+early bulky (EBK) stages showed a higher significant correlation (r = 0.77) for Green Normalized Difference Vegetation indices (GNDVI) with BGB than individual time points. Canopy height measured on the ground correlated well with UAV (CHuav)-based measurements (r = 0.92) at late bulking (LBK) stage. Among different image features, normalized difference red edge index (NDRE) data were found to be consistently highly correlated (r = 0.65 to 0.84) with AGB at LBK stage. (4) Among the four ML algorithms used in this study, k-Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machine (SVM) showed the best performance for root yield prediction with the highest accuracy of R2 = 0.67, 0.66 and 0.64, respectively. Conclusion: UAV platforms, time series image acquisition, automated image analytical framework (CIAT Pheno-i), and key vegetation indices (VIs) to estimate phenotyping traits and root yield described in this work have great potential for use as a selection tool in the modern cassava breeding programs around the world to accelerate germplasm and varietal selection. The image analysis software (CIAT Pheno-i) developed from this study can be widely applicable to any other crop to extract phenotypic information rapidly.

scholarly journals Easy MPE: Extraction of Quality Microplot Images for UAV-Based High-Throughput Field Phenotyping

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Léa Tresch ◽  
Yue Mu ◽  
Atsushi Itoh ◽  
Akito Kaga ◽  
Kazunori Taguchi ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
Field Conditions ◽  
Processing Step ◽  
Field Phenotyping ◽  
Binary Segmentation ◽  
Aerial Vehicle ◽  
Uav Images

Microplot extraction (PE) is a necessary image processing step in unmanned aerial vehicle- (UAV-) based research on breeding fields. At present, it is manually using ArcGIS, QGIS, or other GIS-based software, but achieving the desired accuracy is time-consuming. We therefore developed an intuitive, easy-to-use semiautomatic program for MPE called Easy MPE to enable researchers and others to access reliable plot data UAV images of whole fields under variable field conditions. The program uses four major steps: (1) binary segmentation, (2) microplot extraction, (3) production of ∗.shp files to enable further file manipulation, and (4) projection of individual microplots generated from the orthomosaic back onto the raw aerial UAV images to preserve the image quality. Crop rows were successfully identified in all trial fields. The performance of the proposed method was evaluated by calculating the intersection-over-union (IOU) ratio between microplots determined manually and by Easy MPE: the average IOU (±SD) of all trials was 91% (±3).

scholarly journals Outdoor Plant Segmentation With Deep Learning for High-Throughput Field Phenotyping on a Diverse Wheat Dataset

2022 ◽  
Vol 12 ◽  
Author(s):  
Radek Zenkl ◽  
Radu Timofte ◽  
Norbert Kirchgessner ◽  
Lukas Roth ◽  
Andreas Hund ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Throughput ◽  
Developmental Stages ◽  
Agricultural Practices ◽  
Support Vector ◽  
Learning Approaches ◽  
Support Vector Classifier ◽  
Wide Range ◽  
Field Phenotyping

Robust and automated segmentation of leaves and other backgrounds is a core prerequisite of most approaches in high-throughput field phenotyping. So far, the possibilities of deep learning approaches for this purpose have not been explored adequately, partly due to a lack of publicly available, appropriate datasets. This study presents a workflow based on DeepLab v3+ and on a diverse annotated dataset of 190 RGB (350 x 350 pixels) images. Images of winter wheat plants of 76 different genotypes and developmental stages have been acquired throughout multiple years at high resolution in outdoor conditions using nadir view, encompassing a wide range of imaging conditions. Inconsistencies of human annotators in complex images have been quantified, and metadata information of camera settings has been included. The proposed approach achieves an intersection over union (IoU) of 0.77 and 0.90 for plants and soil, respectively. This outperforms the benchmarked machine learning methods which use Support Vector Classifier and/or Random Forrest. The results show that a small but carefully chosen and annotated set of images can provide a good basis for a powerful segmentation pipeline. Compared to earlier methods based on machine learning, the proposed method achieves better performance on the selected dataset in spite of using a deep learning approach with limited data. Increasing the amount of publicly available data with high human agreement on annotations and further development of deep neural network architectures will provide high potential for robust field-based plant segmentation in the near future. This, in turn, will be a cornerstone of data-driven improvement in crop breeding and agricultural practices of global benefit.

scholarly journals Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population

2022 ◽  
Vol 12 ◽  
Author(s):  
Jana Ebersbach ◽  
Nazifa Azam Khan ◽  
Ian McQuillan ◽  
Erin E. Higgins ◽  
Kyla Horner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Drought Stress ◽  
High Throughput ◽  
Complex Traits ◽  
Phenotypic Diversity ◽  
Crop Improvement ◽  
Breeding Population ◽  
Supervised Machine Learning ◽  
Oilseed Crop

Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.

Evaluation of the performance of cassava (Manihot esculenta Crantz) when grown from inverted stem cuttings

1978 ◽  
Vol 90 (1) ◽  
pp. 149-156 ◽  
Author(s):  
I. C. Onwueme
Keyword(s):  
Field Experiment ◽  
Leaf Area ◽  
Manihot Esculenta ◽  
Unit Area ◽  
Lower Percentage ◽  
Stem Cuttings ◽  
Total Leaf Area ◽  
Manihot Esculenta Crantz ◽  
Below Ground ◽  
Number Of Leaves

SUMMARYIn order to investigate the characteristics of inverted cassava plantings, seven-node cuttings were planted upright or inverted, and such that four nodes of each cutting were within the soil in a glasshouse. They were observed for 7 weeks. The inverted cuttings sprouted less rapidly, but the percentage of the cuttings which eventually sprouted was not decreased. The inverted cuttings produced more sprouts than the upright ones, and a greater percentage of their sprouts were produced below ground than for the upright cuttings. At 5 and 7 weeks, the total leaf area per cutting was less for the inverted cuttings. The number of leaves per cutting did not differ between the treatments.In a field experiment comparing upright and inverted stem pieces, field emergence and tubering were slower in the inverted cuttings. Yield per plant and per unit area was significantly lower for the inverted cuttings. In the upright planting, most of the tubers originated at the lowest extremity of the cutting and formed a compact bunch there. For the inverted planting, most of the tubers arose from the shallower-lying submerged nodes, but the tubers did not form a compact bunch. The inverted planting had a lower percentage of its tubers lying along the ridge. In a few of the inverted plantings, but not in the upright, the bases of some stems became tuberous. The treatments did not differ with respect to mean tuber length, radius of spread of the tuber tip, and the number of major stems per plant.

scholarly journals Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)

2020 ◽  
Author(s):  
Michael Gomez Selvaraj ◽  
Manuel Valderrama ◽  
Diego Guzman ◽  
Milton Valencia ◽  
Henry Ruiz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Vegetation Indices ◽  
Analytical Framework ◽  
Aerial Image ◽  
Root Yield ◽  
Breeding Programs ◽  
Cassava Root ◽  
Below Ground

Abstract Background: Rapid non-destructive measurements to predict cassava root yield over the full growing season through large numbers of germplasm and multiple environments is a huge challenge in Cassava breeding programs. As opposed to waiting until the harvest season, multispectral imagery using unmanned aerial vehicles (UAV) are capable of measuring the canopy metrics and vegetation indices (VIs) traits at different time points of the growth cycle. This resourceful time series aerial image processing with appropriate analytical framework is very important for the automatic extraction of phenotypic features from the image data. Many studies have demonstrated the usefulness of advanced remote sensing technologies coupled with machine learning (ML) approaches for accurate prediction of valuable crop traits. Until now, Cassava has received little to no attention in aerial image-based phenotyping and ML model testing. Results : To accelerate image processing, an automated image-analysis framework called CIAT Pheno-i was developed to extract plot level vegetation indices/canopy metrics. Multiple linear regression models were constructed at different key growth stages of cassava, using ground-truth data and vegetation indices obtained from a multispectral sensor. Henceforth, the spectral indices/features were combined to develop models and predict cassava root yield using different Machine learning techniques. Our results showed that (1) Developed CIAT pheno-i image analysis framework was found to be easier and more rapid than manual methods. (2) The correlation analysis of four phenological stages of cassava revealed that elongation (EL) and late bulking (LBK) were the most useful stages to estimate above-ground biomass (AGB), below-ground biomass (BGB) and canopy height (CH). (3) The multi-temporal analysis revealed that cumulative image feature information of EL+early bulky (EBK) stages showed a higher significant correlation ( r = 0.77) for Green Normalized Difference Vegetation indices (GNDVI) with BGB than individual time points. Canopy height measured on the ground correlated well with UAV (CHuav)-based measurements ( r = 0.92) at late bulking (LBK) stage. Among different image features, normalized difference red edge index (NDRE) data were found to be consistently highly correlated ( r = 0.65 to 0.84) with AGB at LBK stage. (4) Among the four ML algorithms used in this study, k-Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machine (SVM) showed the best performance for root yield prediction with the highest accuracy of R 2 = 0.67, 0.66 and 0.64, respectively. Conclusion : UAV platforms, time series image acquisition, automated image analytical framework (CIAT Pheno-i), and key vegetation indices (VIs) to estimate phenotyping traits and root yield described in this work have great potential for use as a selection tool in the modern cassava breeding programs around the world to accelerate germplasm and varietal selection. The image analysis software (CIAT Pheno-i) developed from this study can be widely applicable to any other crop to extract phenotypic information rapidly.

scholarly journals Easy MPE: Extraction of quality microplot images for UAV-based high-throughput field phenotyping

2019 ◽  
Author(s):  
Léa Tresch ◽  
Yue Mu ◽  
Atsushi Itoh ◽  
Akito Kaga ◽  
Kazunori Taguchi ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
Automatic Program ◽  
Processing Step ◽  
Field Phenotyping ◽  
Binary Segmentation ◽  
Aerial Vehicle ◽  
Uav Images

AbstractMicroplot extraction (MPE) is a necessary image-processing step in unmanned aerial vehicle (UAV)-based research on breeding fields. At present, it is manually using ArcGIS, QGIS or other GIS-based software, but achieving the desired accuracy is time-consuming. We therefore developed an intuitive, easy-to-use semi-automatic program for MPE called Easy MPE to enable researchers and others to access reliable plot data UAV images of whole fields under variable field conditions. The program uses four major steps: (1). Binary segmentation, (2). Microplot extraction, (3). Production of *.shp files to enable further file manipulation, and (4). Projection of individual microplots generated from the orthomosaic back onto the raw aerial UAV images to preserve the image quality. Crop rows were successfully identified in all trial fields. The performance of proposed method was evaluated by calculating the intersection-over-union (IOU) ratio between microplots determined manually and by Easy MPE: The average IOU (±SD) of all trials was 91% (±3).

scholarly journals Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)

2020 ◽  
Author(s):  
Michael Gomez Selvaraj ◽  
Manuel Valderrama ◽  
Diego Guzman ◽  
Milton Valencia ◽  
Henry Ruiz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Vegetation Indices ◽  
Analytical Framework ◽  
Aerial Image ◽  
Root Yield ◽  
Breeding Programs ◽  
Cassava Root ◽  
Below Ground

Abstract Background: Rapid non-destructive measurements to predict cassava root yield over the full growing season through large numbers of germplasm and multiple environments is a huge challenge in Cassava breeding programs. As opposed to waiting until the harvest season, multispectral imagery using unmanned aerial vehicles (UAV) are capable of measuring the canopy metrics and vegetation indices (VIs) traits at different time points of the growth cycle. This resourceful time series aerial image processing with appropriate analytical framework is very important for the automatic extraction of phenotypic features from the image data. Many studies have demonstrated the usefulness of advanced remote sensing technologies coupled with machine learning (ML) approaches for accurate prediction of valuable crop traits. Until now, Cassava has received little to no attention in aerial image-based phenotyping and ML model testing. Results : To accelerate image processing, automated image-analysis framework called CIAT Pheno-i was developed to extract plot level vegetation indices/canopy metrics. Multiple linear regression models were constructed at different key growth stages of cassava, using ground-truth data and vegetation indices obtained from a multispectral sensor. Henceforth, the spectral indices/features were combined to develop models and predict cassava root yield using different Machine learning techniques. Our results showed that (1) Developed CIAT pheno-i image analysis framework was found to be easier and more rapid than manual methods. (2) The correlation analysis of four phenological stages of cassava revealed that elongation (EL) and late bulking (LBK) were the most useful stages to estimate above-ground (AGB), below-ground biomass (BGB) and canopy height (CH). (3) The multi-temporal analysis revealed that cumulative image feature information of EL+EBK stages showed a higher significant correlation ( r = 0.77 for GNDVI) with BGB than individual time points. Canopy height measured on the ground correlated well with UAV (CHuav)-based measurements ( r = 0.92) at late bulking (LBK) stage. Among different image features, normalized difference red edge index (NDRE) data were found to be consistently highly correlated ( r = 0.65 to 0.84) with ABG at LBK stage. (4) Among the four ML algorithms used in this study, k-Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machine (SVM) showed the best performance for root yield prediction with the highest accuracy of R 2 = 0.67, 0.66 and 0.64, respectively. Conclusion : UAV platforms, time series image acquisition, automated image analytical framework (CIAT pheno-i), and key vegetation indices (VIs) to estimate phenotyping traits and root yield described in this work have great potential for use as a selection tool in the modern cassava breeding programs around the world to accelerate germplasm and varietal selection. The image analysis software (CIAT pheno-i) developed from this study can be widely applicable to any other crop to extract phenotypic information rapidly.

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