Late Bulking Research Articles

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)

BackgroundRapid 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.ResultsTo 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.ConclusionUAV 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.

Identification of cassava (Manihot esculenta Crantz) genotypes with early storage root bulking

ABSTRACTEarly bulking (EB) cassava varieties shorten the growth period from planting to harvesting. Cassava is preferred by farmers to many other crops because of its high-yielding potential and ability to survive a 4- to 6-month dry season. However, the crop in some cases is only able to remain alive during the dry season without further starch accumulation. This and some other factors, such as pest destruction during dry season and inability to use the same land for cultivation of other crops such as vegetables, therefore necessitate the development of EB cassava genotypes. The objective of this study was therefore to identify EB cassava genotypes using the difference in fresh storage root yield (FSRY) at different plant ages. Thirty-four cassava genotypes were evaluated at Umudike and Otobi in Nigeria in two seasons at two plant ages of 7 and 12 months after planting (MAP) for FSRY. The data generated were used to classify the cassava genotypes as EB or late bulking (LB). Cassava genotypes with less than 100% increase in FSRY at 12 MAP over the yield at 7 MAP were regarded as EB, whereas those with more than 100% increase were regarded as LB genotypes. This basis was used considering the fact that root thickening in cassava commences at about 2 MAP; hence, the root yield at 7 and 12 MAP represents accumulation of assimilates for 5 and 10 months, respectively. About 65%, 62%, and 79% of the genotypes were classified as EB genotypes at Otobi, Umudike, and across the two locations, respectively.