Recent advances in sensing plant diseases for precision crop protection

Mahlein, Anne-Katrin; Oerke, E. C.; Steiner, Ulrike; Dehne, H. W.

doi:10.1007/s10658-011-9878-z

Cited by 480 publications

(287 citation statements)

References 61 publications

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“…Two important indicators of these parameters-leaf area index (LAI) and above ground biomass (AGB)-were used to monitor crop canopy structural development and growth changes and to estimate yield. The reasonable and reliable estimation of LAI and biomass can improve crop fertilizer applications [2], water irrigation [3,4], disease and weed control [5,6], and grain production marketing [7][8][9]. LAI and biomass change seasonally under different environmental conditions, and therefore, it is important to timely estimate their values.…”

Section: Introductionmentioning

confidence: 99%

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

et al. 2015

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Leaf area index (LAI) and biomass are frequently used target variables for agricultural and ecological remote sensing applications. Ground measurements of winter wheat LAI and biomass were made from March to May 2014 in the Yangling district, Shaanxi, Northwest China. The corresponding remotely sensed data were obtained from the earth-observation satellites Huanjing (HJ) and RADARSAT-2. The objectives of this study were (1) to investigate the relationships of LAI and biomass with several optical spectral vegetation indices (OSVIs) and radar polarimetric parameters (RPPs), (2) , p < 0.01) were highly correlated with biomass. The estimation accuracy of LAI and biomass was better using the COSVI-RPPs than using the OSVIs and RPPs alone. The results revealed that the PLSR regression equation better estimated LAI and biomass than the MSR regression equation based on all the COSVI-RPPs, OSVIs, and RPPs. Our results indicated that the COSVI-RPPs can be used to robustly estimate LAI and biomass. This study may provide a guideline for improving the estimations of LAI and biomass of winter wheat using multisource remote sensing data.

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Section: Introductionmentioning

confidence: 99%

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

et al. 2015

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“…9 The TERRA-MEPP platform being deployed in mature sorghum during August 2016. The top of the mast was raised above the canopy to its tallest height of 3.6 m above the body of the robot clear early in the growing season, which is when the phenotyping measurements for detecting early changes in plant physiology and early biomass predictions occur (Mahlein et al 2012;Montes et al 2011).…”

Section: Field Performance Of the Phenotyping Platformmentioning

confidence: 99%

Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum

2018

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This article describes the design and field evaluation of a low-cost, high-throughput phenotyping robot for energy sorghum for use in biofuel production. High-throughput phenotyping approaches have been used in isolated growth chambers or greenhouses, but there is a growing need for field-based, precision agriculture techniques to measure large quantities of plants at high spatial and temporal resolutions throughout a growing season. A lowcost, tracked mobile robot was developed to collect phenotypic data for individual plants and tested on two separate energy sorghum fields in Central Illinois during summer 2016. Stereo imaging techniques determined plant height, and a depth sensor measured stem width near the base of the plant. A data capture rate of 0.4 ha, bi-weekly, was demonstrated for platform robustness consistent with various environmental conditions and crop yield modeling needs, and formative human-robot interaction observations were made during the field trials to address usability. This work is of interest to researchers and practitioners advancing the field of plant breeding because it demonstrates a new phenotyping platform that can measure individual plant architecture traits accurately (absolute measurement error at 15% for plant height and 13% for stem width) over large areas at a sub-daily frequency; furthermore, the design of this platform can be extended for phenotyping applications in maize or other agricultural row crops.

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“…To exploit the full potential of these highly advanced, pioneering technologies and high dimensional, complex data for precision crop protection, a multi-disciplinary approach including engineering, plant pathology and informatics is required. Besides precision crop protection, plant phenotyping for resistance breeding or fungicide selection can be optimized by these inventive technologies [4].…”

Section: In Diseasementioning

confidence: 99%

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2017

MOJPB

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Remote sensing (RS) is used for identify, measure and analyze characteristics of objects of interest without direct contact; and Geographic Information System (GIS) facilitates storing, manipulating and retrieving spatially referenced data. Together RS and GIS offer the ability of rapid collection, process and integrate data and display results in geographic referenced maps and reports. Satellite remote sensing techniques may offer a proficient and cost-effective means of acquiring timely data for the advancement and management of natural resources. RS and GIS is the only technique that can provide holistic approach to study the environment and interrelationships that exist within the different biophysical components. Integrated RS and GIS technology offers incredible opportunities in agriculture for cost effective management of causes of crop stress. Its application is rapidly increasing in the area of hydrogeology and water resources development and can investigate urban landscape, physiognomy, lakes, plants, places of interest, land consumption, building and population distribution rapidly. In agriculture, it provides the solid technological foundation for agricultural statistics. Agriculturalists can measure growth rate changes caused by stress like salinity, drought, temperature, etc.It can also be applied for coastal environment management and monitoring due to its effective data acquiring capacity and resource management. Satellite remote sensing is a promising tool for conservation issues and is instrumental in environment impact assessment. Plant disease detection due to environmental and epidemiological factors can be monitored in crop stands. Contribution to human health has been done by utilizing the intelligence on the distribution and abundance of cyanobacteria in internal water bodies thus aid risk assessment and management. It is now possible to synergistically combine lower resolution geostationary ocean color sensor with high resolution polar orbiting sensor to provide frequency with high special resolution. This article provides a survey of applications and recent theoretical developments in the context of remote sensing data analysis and GIS.

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Recent advances in sensing plant diseases for precision crop protection

Cited by 480 publications

References 61 publications

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum

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