Non-Invasive Spectral Phenotyping Methods can Improve and Accelerate Cercospora Disease Scoring in Sugar Beet Breeding

Jansen, Marcus; Bergsträsser, Sergej; Schmittgen, Simone; Müller-Linow, Mark; Rascher, Uwe

doi:10.3390/agriculture4020147

Cited by 18 publications

(15 citation statements)

References 28 publications

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“…A recent study on evaluating sugar beet varieties for Cercospora resistance indicated that simple vegetation indices such as normalized difference vegetation index (NDVI), leaf water index (LWI) and Cercospora Leaf Spot Index (CLSI) could be correlated with disease severity (Jansen et al, 2014). In WSU's ongoing study, 20 (Russets) and 30 (20 Russets + 10 Chip and Specialty) potato selections/lines were screened with two replications to identify varieties resistant and susceptible to viral (potato virus Y) and early die (Verticillium wilt) diseases, respectively.…”

Section: Plant Biotic Stressmentioning

confidence: 99%

Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review

Sankaran

Khot

Espinoza

et al. 2015

European Journal of Agronomy

414

293

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Section: Plant Biotic Stressmentioning

confidence: 99%

Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review

Sankaran

Khot

Espinoza

et al. 2015

European Journal of Agronomy

414

293

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“…Besides UAVs or other airborne platforms, ground-based imaging systems have been evaluated in studies concerning disease assessment based on optical data [26][27][28][29][30]. In this case, the authors also focused on pathogens affecting different perennial and annual crops (e.g., Huanglongbing in citrus, cercospora leaf spot in sugarbeet, tulip breaking virus, yellow rust and fusarium head blight in wheat and barley) and tested several methods for discriminating diseased and healthy plants, or to quantify disease severity, achieving variable discriminative potential and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato

et al. 2019

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Assessment of disease incidence and severity at farm scale or in agronomic trials is frequently performed based on visual crop inspection, which is a labor intensive task prone to errors associated with its subjectivity. Therefore, alternative methods to relate disease incidence and severity with changes in crop traits are of great interest. Optical imagery in the visible and near-infrared (Vis-NIR) can potentially be used to detect changes in crop traits caused by pathogen development. Also, cameras on-board of Unmanned Aerial Vehicles (UAVs) have flexible data collection capabilities allowing adjustments considering the trade-off between data throughput and its resolution. However, studies focusing on the use of UAV imagery to describe changes in crop traits related to disease infection are still lacking. More specifically, evaluation of late blight (Phytophthora infestans) incidence in potato concerning early discrimination of different disease severity levels has not been extensively reported. In this article, the description of spectral changes related to the development of potato late blight under low disease severity levels is performed using sub-decimeter UAV optical imagery. The main objective was to evaluate the sensitivity of the data acquired regarding early changes in crop traits related to disease incidence. For that, UAV images were acquired on four dates during the growing season (from 37 to 78 days after planting), before and after late blight was detected in the field. The spectral variability observed in each date was summarized using Simplex Volume Maximization (SiVM), and its relationship with experimental treatments (different crop systems) and disease severity levels (evaluated by visual assessment) was determined based on pixel-wise log-likelihood ratio (LLR) calculation. Using this analytical framework it was possible to identify considerable spectral changes related to late blight incidence in different treatments and also to disease severity level as low as between 2.5 and 5.0% of affected leaf area. Comparison of disease incidence and spectral information acquired using UAV (with 4-5 cm of spatial resolution) and ground-based imagery (with 0.1-0.2 cm of spatial resolution) indicate that UAV data allowed identification of patterns comparable to those described by ground-based images, despite some differences concerning the distribution of affected areas detected within the sampling units and an attenuation in the signal measured. Finally, although aggregated information at sampling unit level provided discriminative potential for higher levels of disease development, focusing on spectral information related to disease occurrence increased the discriminative potential of the data acquired.

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“…In the last decade, several UAV‐based agricultural management applications were proposed, including methods to monitor drought and nutrient stress (Berni et al., 2009; Gago et al., 2017; González‐Dugo et al., 2013; Severtson et al., 2016; Zaman‐Allah et al., 2015), to treat pests and diseases (Calderón, Navas Cortés, Lucena León, & Zarco‐Tejada, 2013; Calderón, Montes‐Borrego, Landa, Navas‐Cortés, & Zarco‐Tejada, 2014; Garcia‐Ruiz et al., 2013, Jansen, Bergsträsser, Schmittgen, Müller‐Linow, & Rascher, 2014), and to measure productivity (Bendig et al., 2015; Holman et al., 2016; Maresma, Ariza, Martínez, Lloveras, & Martínez‐Casasnovas, 2016). Although these methods were developed for UAV‐collected data, they required precise calibration in order to provide accurate information on plant status.…”

Section: Flying Closer To the Target: Navs And Mavs Open New Opportunmentioning

confidence: 99%

Nano and Micro Unmanned Aerial Vehicles (UAVs): A New Grand Challenge for Precision Agriculture?

et al. 2020

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By collecting data at spatial and temporal scales that are inaccessible to satellite and field observation, unmanned aerial vehicles (UAVs) are revolutionizing a number of scientific and management disciplines. UAVs may be particularly valuable for precision agricultural applications, offering strong potential to improve the efficiency of water, nutrient, and disease management. However, some authors have suggested that the UAV industry has overhyped the potential value of this technology for agriculture, given that it is difficult for non‐specialists to operate UAVs as well as to process and interpret the resulting data. Here, we analyze the barriers to applying UAVs for precision agriculture, which range from regulatory issues to technical requirements. We then evaluate how new developments in the nano‐ and micro‐UAV (NAV and MAV, respectively) markets may help to overcome these barriers. Among the possible breakthroughs that we identify is the ability of NAV/MAV platforms to directly quantify plant traits using methods (e.g., object‐oriented classification) that require less image calibration and interpretation than spectral index–based approaches. We suggest that this potential, when combined with steady improvements in sensor miniaturization, flight precision, and autonomy as well as cloud‐based image processing, will make UAVs a tool with much broader adoption by agricultural managers in the near future. If this wider uptake is realized, then UAVs have real potential to improve agriculture's resource‐use efficiency. © 2020 by John Wiley & Sons, Inc.

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Non-Invasive Spectral Phenotyping Methods can Improve and Accelerate Cercospora Disease Scoring in Sugar Beet Breeding

Cited by 18 publications

References 28 publications

Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review

Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review

Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato

Nano and Micro Unmanned Aerial Vehicles (UAVs): A New Grand Challenge for Precision Agriculture?

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