Abstract
Cassava as a world food security crop still suffers from an inadequate means to measure early storage root bulking (ESRB), a trait that describes early maturity and a key characteristic of improved cassava varieties. The objective of this study is to evaluate the capability of ground penetrating radar (GPR) for non-destructive assessment of cassava root biomass. GPR was evaluated for this purpose in a field trial conducted in Ibadan, Nigeria. Different methods of processing the GPR radargram were tested, which included time slicing the radargram below the antenna surface in order to reduce ground clutter; to remove coherent sub-horizontal reflected energy; and having the diffracted energy tail collapsed into representative point of origin. GPR features were then extracted using Discrete Fourier Transformation (DFT), and Bayesian Ridge Regression (BRR) models were developed considering one, two and three-way interactions. Prediction accuracies based on Pearson correlation coefficient (r) and coefficient of determination (R2) were estimated by the linear regression of the predicted and observed root biomass. A simple model without interaction produced the best prediction accuracy of r = 0.64 and R2 = 0.41. Our results demonstrate that root biomass can be predicted using GPR and it is expected that the technology will be adopted by cassava breeding programs for selecting early stage root bulking during the crop growth season as a novel method to dramatically increase crop yield.
Highlights
Introduction published maps and institutional affilOf the 820 million undernourished people worldwide, most are living in Africa [1] with poverty and global warming contributing to the ever-worsening scourge of malnutrition.This situation calls for additional research into staple crops that are well adapted to the tropics, tolerate erratic weather, and thrive even in drought and low-input farming systems.No other crop is more suitable for this purpose than cassava
Results from all the data processing pipelines (P0 to P3) and Bayesian models (Baseline to Model 4 (M4)) showed that prediction accuracy increases if ground penetrating radar (GPR) spectra are introduced as a covariate
Aside from utilizing a high-throughput antenna and a larger sample size, this study presents the first non-destructive and unsupervised root assessment in cassava
Summary
Introduction published maps and institutional affilOf the 820 million undernourished people worldwide, most are living in Africa [1] with poverty and global warming contributing to the ever-worsening scourge of malnutrition.This situation calls for additional research into staple crops that are well adapted to the tropics, tolerate erratic weather, and thrive even in drought and low-input farming systems.No other crop is more suitable for this purpose than cassava. Of the 820 million undernourished people worldwide, most are living in Africa [1] with poverty and global warming contributing to the ever-worsening scourge of malnutrition This situation calls for additional research into staple crops that are well adapted to the tropics, tolerate erratic weather, and thrive even in drought and low-input farming systems. Pipeline 0 indicates the top noise removed while (D,E) indicates the low frequency noises contrast of reflection is sharpened with background correction in (F,G) and Pipeline 3. Horizontal bands of noise are removed as well as the contrast of reflection is the band of energy resulting from the collapsed diffraction tails as compared to (D). (I) shows the combination of reduced and collapsed energy, making the reflections sharper Horizontal bands of noise are removed as well as the contrast of reflection is the band of energy resulting from the collapsed diffraction tails as compared to (D). (I) shows the combination of reduced sharpened with energy, background correction (F,G) and Pipeline 3. (H) shows the reduced thickness of noise and collapsed making the reflectionsin sharper.
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