A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization

Vergara-Díaz, Omar; Zaman-Allah, Mainassara; Masuka, Benhildah Pamhidzai; Hornero, Alberto; Zarco‐Tejada, Pablo J.; Prasanna, Boddupalli M.; Cairns, Jill E.; Araus, José Luís

doi:10.3389/fpls.2016.00666

Cited by 116 publications

(100 citation statements)

References 56 publications

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“…Tanger et al (2017) used a combination of tractormounted multispectral reflectance and ultrasonic sensors to detect manually validated QTL associated with biomass in rice 4 . Similar measurements have been deployed in other field-grown crops using ground vehicles, aerial vehicles and gantries requiring investment in equipment that often exceeds $100,000s -$1,000,000s 22,23 . In addition to the expense of HTP equipment, many techniques under development require extensive research infrastructure, permits (e.g.…”

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confidence: 91%

High Fidelity Detection of Crop Biomass QTL From Low-Cost Imaging in the Field

Banan

Paul

Feldman

et al. 2017

Preprint

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Above-ground biomass production is a key target for studies of crop abiotic stress tolerance, disease resistance and yield improvement. However, biomass is slow and laborious to evaluate in the field using traditional destructive methods. High-throughput phenotyping (HTP) is widely promoted as a potential solution that can rapidly and non-destructively assess plant traits by exploiting advances in sensor and computing technology. A key potential application of HTP is for quantitative genetics studies that identify loci where allelic variation is associated with variation in crop production. And, the value of performing such studies in the field, where environmental conditions match that of production farming, is recognized. To date, HTP of biomass productivity in field trials has largely focused on expensive and complex methods, which – even if successful – will limit their use to a subset of wealthy research institutions and companies with extensive research infrastructure and highly-trained personnel. Even with investment in ground vehicles, aerial vehicles and gantry systems ranging from thousands to millions of dollars, there are very few examples where Quantitative trait loci (QTLs) detected by HTP of biomass production in a field-grown crop are shown to match QTLs detected by direct measures of biomass traits by destructive harvest techniques. Until such proof of concept for HTP proxies is generated it is unlikely to replace existing technology and be widely adopted. Therefore, there is a need for methods that can be used to assess crop performance by small teams with limited training and at field sites that are remote or have limited infrastructure. Here we use an inexpensive and simple, miniaturized system of hemispherical imaging and light attenuation modeling to identify the same set of key QTLs for biomass production as traditional destructive harvest methods applied to a field-grown Setaria mapping population. This provides a case study of a HTP technology that can deliver results for QTL mapping without high costs or complexity.

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confidence: 91%

High Fidelity Detection of Crop Biomass QTL From Low-Cost Imaging in the Field

Banan

Paul

Feldman

et al. 2017

Preprint

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“…Over the last decade, there has been a growing interest in exploring high-throughput imaging techniques to dissect quantitative crop phenotypic traits [3,4]. Visible light imaging has been widely reported among other imaging techniques.…”

Section: Introductionmentioning

confidence: 99%

High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging

Makanza¹,

Zaman-Allah²,

Cairns³

et al. 2018

Remote Sensing

106

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Abstract:In the crop breeding process, the use of data collection methods that allow reliable assessment of crop adaptation traits, faster and cheaper than those currently in use, can significantly improve resource use efficiency by reducing selection cost and can contribute to increased genetic gain through improved selection efficiency. Current methods to estimate crop growth (ground canopy cover) and leaf senescence are essentially manual and/or by visual scoring, and are therefore often subjective, time consuming, and expensive. Aerial sensing technologies offer radically new perspectives for assessing these traits at low cost, faster, and in a more objective manner. We report the use of an unmanned aerial vehicle (UAV) equipped with an RGB camera for crop cover and canopy senescence assessment in maize field trials. Aerial-imaging-derived data showed a moderately high heritability for both traits with a significant genetic correlation with grain yield. In addition, in some cases, the correlation between the visual assessment (prone to subjectivity) of crop senescence and the senescence index, calculated from aerial imaging data, was significant. We concluded that the UAV-based aerial sensing platforms have great potential for monitoring the dynamics of crop canopy characteristics like crop vigor through ground canopy cover and canopy senescence in breeding trial plots. This is anticipated to assist in improving selection efficiency through higher accuracy and precision, as well as reduced time and cost of data collection.

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“…On the other hand, remote images of the field are captured with sensors attached to aerial platforms [36][37][38][39] for trait estimation. Recent studies to quantify plant canopy development from images either report trait comparisons with reference to a different sensor technology such as LiDAR [16,40] or compare image-based estimation techniques with manual methods [5,41].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

et al. 2018

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Abstract:This study evaluates an aerial and ground imaging platform for assessment of canopy development in a wheat field. The dependence of two canopy traits, height and vigour, on fertilizer treatment was observed in a field trial comprised of ten varieties of spring wheat. A custom-built mobile ground platform (MGP) and an unmanned aerial vehicle (UAV) were deployed at the experimental site for standard red, green and blue (RGB) image collection on five occasions. Meanwhile, reference field measurements of canopy height and vigour were manually recorded during the growing season. Canopy level estimates of height and vigour for each variety and treatment were computed by image analysis. The agreement between estimates from each platform and reference measurements was statistically analysed. Estimates of canopy height derived from MGP imagery were more accurate (RMSE = 3.95 cm, R 2 = 0.94) than estimates derived from UAV imagery (RMSE = 6.64 cm, R 2 = 0.85). In contrast, vigour was better estimated using the UAV imagery (RMSE = 0.057, R 2 = 0.57), compared to MGP imagery (RMSE = 0.063, R 2 = 0.42), albeit with a significant fixed and proportional bias. The ability of the platforms to capture differential development of traits as a function of fertilizer treatment was also investigated. Both imaging methodologies observed a higher median canopy height of treated plots compared with untreated plots throughout the season, and a greater median vigour of treated plots compared with untreated plots exhibited in the early growth stages. While the UAV imaging provides a high-throughput method for canopy-level trait determination, the MGP imaging captures subtle canopy structures, potentially useful for fine-grained analyses of plants.

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A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization

Cited by 116 publications

References 56 publications

High Fidelity Detection of Crop Biomass QTL From Low-Cost Imaging in the Field

High Fidelity Detection of Crop Biomass QTL From Low-Cost Imaging in the Field

High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

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