High-throughput non-destructive biomass determination during early plant development in maize under field conditions

Montes, Juan M.; Technow, Frank; Dhillon, B. S.; Mauch, Franz J.; Melchinger, Albrecht E.

doi:10.1016/j.fcr.2010.12.017

Cited by 134 publications

(116 citation statements)

References 25 publications

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“…The first flight of the day takes about 15 min to setup, while turn-around flights take about 5 min. Alternative estimates include the types of FBP vehicle described by [1] and others, as well as approaches that utilize laser detection of early biomass [21], for example, or that deploy methods that use near infrared sensors to detect vegetation cover. In addition to the time and labour savings associated with aerial detection, this speed and altitude of the platform assists in dealing with issues of variation in sun conditions that affect the quality of photography and non-active near-infrared methods compared to conventional aircraft.…”

Section: Estimation Of Ground Cover Of Hybrids In a Sorghum Breeding mentioning

confidence: 99%

Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping

et al. 2014

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Plant breeding trials are extensive (100s to 1000s of plots) and are difficult and expensive to monitor by conventional means, especially where measurements are time-sensitive. For example, in a land-based measure of canopy temperature (hand-held infrared thermometer at two to 10 plots per minute), the atmospheric conditions may change greatly during the time of measurement. Such sensors measure small spot samples (2 to 50 cm 2 ), whereas image-based methods allow the sampling of entire plots (2 to 30 m 2 ). Capturing images from an aircraft which is flown precisely at low altitude (10 to 40 m) to obtain high ground resolution data for every plot allows the rapid measurement of large numbers of plots. This OPEN ACCESSAgronomy 2014, 4 280 paper outlines the implementation of a customized robotic helicopter (gas-powered, 1.78-m rotor diameter) with autonomous flight control and software to plan flights over experiments that were 0.5 to 3 ha in area and, then, to extract, straighten and characterize multiple experimental field plots from images taken by three cameras. With a capacity to carry 1.5 kg for 30 min or 1.1 kg for 60 min, the system successfully completed >150 flights for a total duration of 40 h. Example applications presented here are estimations of the variation in: ground cover in sorghum (early season); canopy temperature in sugarcane (mid-season); and three-dimensional measures of crop lodging in wheat (late season). Together with this hardware platform, improved software to automate the production of ortho-mosaics and digital elevation models and to extract plot data would further benefit the development of high-throughput field-based phenotyping systems.

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Section: Estimation Of Ground Cover Of Hybrids In a Sorghum Breeding mentioning

confidence: 99%

Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping

et al. 2014

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“…The methods of phenomics include automated, robotic, and/or computer-aided technologies which aim to provide a high throughput of individuals (see Furbank 2009, and papers in the same issue of Functional Plant Biology). These measures can be applied in glasshouses (Sirault et al 2009;Montes et al 2011;White et al 2012) or aerial vehicles (Merz and Chapman 2006;Lelong et al 2008;Berni et al 2009). Breeding companies have a strong demand for technologies that operate in realistic field-evaluation conditions.…”

Section: 'Building' and Delivering New Varietiesmentioning

confidence: 99%

Plant adaptation to climate change—opportunities and priorities in breeding

et al. 2012

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Abstract. Climate change in Australia is expected to influence crop growing conditions through direct increases in elevated carbon dioxide (CO 2 ) and average temperature, and through increases in the variability of climate, with potential to increase the occurrence of abiotic stresses such as heat, drought, waterlogging, and salinity. Associated effects of climate change and higher CO 2 concentrations include impacts on the water-use efficiency of dryland and irrigated crop production, and potential effects on biosecurity, production, and quality of product via impacts on endemic and introduced pests and diseases, and tolerance to these challenges. Direct adaptation to these changes can occur through changes in crop, farm, and value-chain management and via economically driven, geographic shifts where different production systems operate. Within specific crops, a longer term adaptation is the breeding of new varieties that have an improved performance in 'future' growing conditions compared with existing varieties.In crops, breeding is an appropriate adaptation response where it complements management changes, or when the required management changes are too expensive or impractical. Breeding requires the assessment of genetic diversity for adaptation, and the selection and recombining of genetic resources into new varieties for production systems for projected future climate and atmospheric conditions. As in the past, an essential priority entering into a 'climate-changed' era will be breeding for resistance or tolerance to the effects of existing and new pests and diseases. Hence, research on the potential incidence and intensity of biotic stresses, and the opportunities for breeding solutions, is essential to prioritise investment, as the consequences could be catastrophic. The values of breeding activities to adapt to the five major abiotic effects of climate change (heat, drought, waterlogging, salinity, and elevated CO 2 ) are more difficult to rank, and vary with species and production area, with impacts on both yield and quality of product. Although there is a high likelihood of future increases in atmospheric CO 2 concentrations and temperatures across Australia, there is uncertainty about the direction and magnitude of rainfall change, particularly in the northern farming regions. Consequently, the clearest opportunities for 'in-situ' genetic gains for abiotic stresses are in developing better adaptation to higher temperatures (e.g. control of phenological stage durations, and tolerance to stress) and, for C 3 species, in exploiting the (relatively small) fertilisation effects of elevated CO 2 . For most cultivated plant species, it remains to be demonstrated how much genetic variation exists for these traits and what value can be delivered via commercial varieties. Biotechnology-based breeding technologies (marker-assisted breeding and genetic modification) will be essential to accelerate genetic gain, but their application requires additional investment in the understanding, genetic characterisation,...

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“…In addition, most developed FB-HTP platforms incorporate sets of multiple sensors, creating complementary streams of data that, when combined, provide more information than what individual sensors alone can achieve (termed sensor fusion). In one of the first demonstrations of FB-HTP, Montes et al (2011) developed a platform carrying light curtains (measure of canopy height) and spectral reflectance sensors (canopy reflectance) to predict aboveground biomass accumulation in maize (Zea mays). Their results showed that the combination of data from both sensors was more predictive than data from either sensor alone.…”

Section: Plant Canopy Phenotypingmentioning

confidence: 99%

The quest for understanding phenotypic variation via integrated approaches in the field environment

et al. 2016

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High-throughput non-destructive biomass determination during early plant development in maize under field conditions

Cited by 134 publications

References 25 publications

Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping

Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping

Plant adaptation to climate change—opportunities and priorities in breeding

The quest for understanding phenotypic variation via integrated approaches in the field environment

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