Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques

Buchaillot, Ma. Luisa; Gracia‐Romero, Adrian; Vergara-Díaz, Omar; Zaman-Allah, Mainassara; Tarekegne, Amsal; Cairns, Jill E.; Prasanna, Boddupalli M.; Araus, J. L.; Kefauver, Shawn C.

doi:10.3390/s19081815

Cited by 66 publications

(68 citation statements)

References 90 publications

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“…Regarding its performance in assessing yield, on one side, the leaf relative chlorophyll content measured with the leaf sensor did not correlate well with GY. These readings provide useful information for diagnosing plant N status and, by the time N is a limiting factor, it may work efficiently as a GY predictor [78]. However, without nutrient restrictions, the leaf chlorophyll content-GY relationship is not so clear.…”

Section: Ability Of the Remote Sensing Measurements To Assess Genotypmentioning

confidence: 99%

UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat

et al. 2019

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Climate change is one of the primary culprits behind the restraint in the increase of cereal crop yields. In order to address its effects, effort has been focused on understanding the interaction between genotypic performance and the environment. Recent advances in unmanned aerial vehicles (UAV) have enabled the assembly of imaging sensors into precision aerial phenotyping platforms, so that a large number of plots can be screened effectively and rapidly. However, ground evaluations may still be an alternative in terms of cost and resolution. We compared the performance of red-green-blue (RGB), multispectral, and thermal data of individual plots captured from the ground and taken from a UAV, to assess genotypic differences in yield. Our results showed that crop vigor, together with the quantity and duration of green biomass that contributed to grain filling, were critical phenotypic traits for the selection of germplasm that is better adapted to present and future Mediterranean conditions. In this sense, the use of RGB images is presented as a powerful and low-cost approach for assessing crop performance. For example, broad sense heritability for some RGB indices was clearly higher than that of grain yield in the support irrigation (four times), rainfed (by 50%), and late planting (10%). Moreover, there wasn't any significant effect from platform proximity (distance between the sensor and crop canopy) on the vegetation indexes, and both ground and aerial measurements performed similarly in assessing yield.2 of 25 principal goal for breeders. Durum wheat is, by extension, the main cereal cultivated on the southern and eastern shores of the Mediterranean Basin and one of the main cereals in southern Europe [4].Yield is a phenotypically complicated trait, not only because of its genetic complexity [5], but also due to the relative magnitude of gene-environment interactions [6,7], and it is one of the most integrative traits influenced by known and unknown factors. Thus, genotype evaluations in multi-environment trials are needed, at least in the advanced (generations) stages of selection. However, the major point at issue is that high-throughput plant phenotyping (HTPP) may still represent a bottleneck in breeding programs [7], owing to the need to increase the accuracy, precision, and throughput of the methodologies used, while reducing costs and minimizing labor [8,9]. Furthermore, HTPP approaches should allow multi-temporal trait-specific measurements to evaluate the yield components at different phenological moments.Nowadays, and almost by definition, HTPP implies the use of non-invasive remote sensing approaches of different nature [5,10], given the possibility of screening larger populations faster than conventional phenotyping procedures. Moreover, recent progress and advances in the technology of aeronautics and sensors have allowed the adoption of unmanned aerial vehicle (UAV) platforms, capable of precisely screening hundreds of plots in a short period of time [7,11]. Further benefits of the simultaneous c...

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Section: Ability Of the Remote Sensing Measurements To Assess Genotypmentioning

confidence: 99%

UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat

et al. 2019

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“…However, the potential of the relative leaf chlorophyll readings for predicting GY in maize could vary depending on the phenological stage when measurements are taken. Buchaillot et al 47 studied the variations in SPAD measures in assessing GY differences over two phenological stages before grain filling and reported higher correlations during the vegetative stage rather than during flowering. Monneveux et al 48 reported no significant correlations between SPAD and GY during neither middle nor late grain filling.…”

Section: Evaluation Of Leaf-based and Whole-canopy Measurements For Mmentioning

confidence: 99%

Leaf versus whole-canopy remote sensing methodologies for crop monitoring under conservation agriculture: a case of study with maize in Zimbabwe

Gracia‐Romero

Kefauver

Vergara-Díaz

et al. 2020

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Enhancing nitrogen fertilization efficiency for improving yield is a major challenge for smallholder farming systems. Rapid and cost-effective methodologies with the capability to assess the effects of fertilization are required to facilitate smallholder farm management. This study compares maize leaf and canopy-based approaches for assessing N fertilization performance under different tillage, residue coverage and top-dressing conditions in Zimbabwe. Among the measurements made on individual leaves, chlorophyll readings were the best indicators for both N content in leaves (R < 0.700) and grain yield (GY) (R < 0.800). Canopy indices reported even higher correlation coefficients when assessing GY, especially those based on the measurements of the vegetation density as the green area indices (R < 0.850). Canopy measurements from both ground and aerial platforms performed very similar, but indices assessed from the UAV performed best in capturing the most relevant information from the whole plot and correlations with GY and leaf N content were slightly higher. Leaf-based measurements demonstrated utility in monitoring N leaf content, though canopy measurements outperformed the leaf readings in assessing GY parameters, while providing the additional value derived from the affordability and easiness of using a pheno-pole system or the high-throughput capacities of the UAVs.

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“…In addition, it is possible to equip UAVs with a variety of imaging sensors. These factors have increased the effectiveness of UAVs as a tool for precision agriculture and crop monitoring [2][3][4][5][6][7][8]. Additionally, RGB frame imagery can be useful for automating hyperspectral data orthorectification processes, allowing prediction of biomass and other phenotypic factors [9].…”

Section: Introductionmentioning

confidence: 99%

New Strategies for Time Delay Estimation during System Calibration for UAV-Based GNSS/INS-Assisted Imaging Systems

et al. 2019

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The need for accurate 3D spatial information is growing rapidly in many of today’s key industries, such as precision agriculture, emergency management, infrastructure monitoring, and defense. Unmanned aerial vehicles (UAVs) equipped with global navigation satellite systems/inertial navigation systems (GNSS/INS) and consumer-grade digital imaging sensors are capable of providing accurate 3D spatial information at a relatively low cost. However, with the use of consumer-grade sensors, system calibration is critical for accurate 3D reconstruction. In this study, ‘consumer-grade’ refers to cameras that require system calibration by the user instead of by the manufacturer or other high-end laboratory settings, as well as relatively low-cost GNSS/INS units. In addition to classical spatial system calibration, many consumer-grade sensors also need temporal calibration for accurate 3D reconstruction. This study examines the accuracy impact of time delay in the synchronization between the GNSS/INS unit and cameras on-board UAV-based mapping systems. After reviewing existing strategies, this study presents two approaches (direct and indirect) to correct for time delay between GNSS/INS recorded event markers and actual time of image exposure. Our results show that both approaches are capable of handling and correcting this time delay, with the direct approach being more rigorous. When a time delay exists and the direct or indirect approach is applied, horizontal accuracy of 1–3 times the ground sampling distance (GSD) can be achieved without either the use of any ground control points (GCPs) or adjusting the original GNSS/INS trajectory information.

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Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques

Cited by 66 publications

References 90 publications

UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat

UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat

Leaf versus whole-canopy remote sensing methodologies for crop monitoring under conservation agriculture: a case of study with maize in Zimbabwe

New Strategies for Time Delay Estimation during System Calibration for UAV-Based GNSS/INS-Assisted Imaging Systems

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