Cassava breeding and agronomy in Asia: 50 years of history and future directions

Malik, Al Imran; Kongsil, Pasajee; Nguyen, Vu Anh; Ou, Wenjun; Sholihin,; Srean, Pao; Sheela, M.N.; López-Lavalle, Luís Augusto Becerra; Utsumi, Yoshinori; Lü, Cheng; Kittipadakul, Piya; Nguyễn, Hữu Hỷ; Ceballos, Hernán; Nguyễn, Trọng Hiển; Gomez, Michael Selvaraj; Aiemnaka, Pornsak; Labarta, Ricardo; Chen, Songbi; Amawan, Suwaluk; Sok, Sophearith; Youabee, Laothao; Seki, Motoaki; Tokunaga, Hiroki; Wang, Wenquan; Li, Kaimian; Nguyễn, Hai Anh; Nguyễn, Văn Đồng; Hàm, Lê Huy; Ishitani, Manabu

doi:10.1270/jsbbs.18180

Cited by 96 publications

(98 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The conventional breeding approach described above has been successful in developing outstanding cassava varieties [22] Optimizing the genotype by environment interaction is key to sustaining crop production gains [26]. Cassava is a resilient crop but, in spite of some misconceptions, it is very responsive to good agronomic practices [27].…”

Section: Genetic Gains In Cassava From the Conventional Breeding Systemmentioning

confidence: 99%

Excellence in Cassava Breeding: Perspectives for the Future

2020

Crop Breed Genet Genom.

View full text Add to dashboard Cite

Cassava (Manihot esculenta Crantz) is a major tropical crop. Regarded as an orphan crop a few decades ago, it now receives considerable attention from governments, industry and agencies investing in agricultural research. As a result, the cassava community generates vast amounts of information and develops useful technologies and products. This positions cassava as a key industrial commodity and a reliable food security staple. Significant genetic gains have been achieved through the early 2000s. Gains are particularly noticeable under better agronomic conditions, as was the case for the green revolution of cereals. However, further gains obtained in the past two decades have not been as impressive. Cassava breeding cycle is long, and its multiplication rate slow. Therefore, it takes no less than 8 years to develop a new variety. Cassava breeding is based on the use of heterozygous progenitors which has important drawbacks. One of them is the impossibility to implement conventional back-crossing. Cassava researchers have recently introgressed a single recessive trait (amylose-free starch) into elite varieties. This was unprecedented in cassava and revealed important problems incorporating single genes into elite varieties. In the absence of backcross, the process required essentially the development of new varieties, which exposed strong effects of undesirable genetic linkages. Breeding approaches to overcome the problems of introgressing single-gene traits have been developed and will be implemented to introduce resistance to cassava mosaic disease into SE Asia breeding populations. These methods will also be useful to exploit recently identified immunity to cassava brown streak disease, a serious problem currently restricted to Africa. Trait introgression is an important process in crop breeding and will be more important in the future as gene discovery and editing identify useful genes. This article consolidates and integrates information from cassava and other crops and proposes

show abstract

Section: Genetic Gains In Cassava From the Conventional Breeding Systemmentioning

confidence: 99%

Excellence in Cassava Breeding: Perspectives for the Future

2020

Crop Breed Genet Genom.

View full text Add to dashboard Cite

show abstract

“…[79]. Early detection and crop management associated with yield limitations can help increase productivity [4,23,80]. Crop yield prediction models could aid in early decision-making, optimizing the time required for eld evaluation, thus reducing the resources allocated to the research programs [81].…”

Section: Cassava Root Yield Predictions Using ML Modelsmentioning

confidence: 99%

“…Its versatile nature, it is often referred to as the "drought, war and famine crop of the developing world" [3], places it among the most adaptive crops during climate change. Early vigor, rapid root bulking, higher root yield, resistance to major pest and diseases, waxy cassava are the most important targeted traits in cassava breeding programs around the world [4].…”

Section: Introductionmentioning

confidence: 99%

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)

Selvaraj

Valderrama

Guzmán

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…[79]. Early detection and crop management associated with yield limitations can help increase productivity [4,23,80]. Crop yield prediction models could aid in early decision-making, optimizing the time required for field evaluation, thus reducing the resources allocated to the research programs [81].…”

Section: Cassava Root Yield Predictions Using ML Modelsmentioning

confidence: 99%

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)

Selvaraj

Valderrama

Guzmán

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Cassava breeding and agronomy in Asia: 50 years of history and future directions

Cited by 96 publications

References 87 publications

Excellence in Cassava Breeding: Perspectives for the Future

Excellence in Cassava Breeding: Perspectives for the Future

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)

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)

Contact Info

Product

Resources

About