87 research outputs found

    A review of neural networks in plant disease detection using hyperspectral data

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    © 2018 China Agricultural University This paper reviews advanced Neural Network (NN) techniques available to process hyperspectral data, with a special emphasis on plant disease detection. Firstly, we provide a review on NN mechanism, types, models, and classifiers that use different algorithms to process hyperspectral data. Then we highlight the current state of imaging and non-imaging hyperspectral data for early disease detection. The hybridization of NN-hyperspectral approach has emerged as a powerful tool for disease detection and diagnosis. Spectral Disease Index (SDI) is the ratio of different spectral bands of pure disease spectra. Subsequently, we introduce NN techniques for rapid development of SDI. We also highlight current challenges and future trends of hyperspectral data

    Early detection of rice blast (Pyricularia) at seedling stage in Nipponbare rice variety using near-infrared hyper-spectral image

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    Blast rice is the worst biological disaster in rice cultivation. It reduces the yield at least up to 40 to 50% (in the worst period of disease). In this study, the near-infrared hyper-spectral image was investigated to detect blast rice in Nipponbare at seedling stage. Two hundred rice seedlings were segregated into two classes: infected and healthy. All of rice seedlings were scanned with a hyper-spectral imaging system in the NIR (900 to 1700 nm) wavelength range. Principal component analysis (PCA) was performed on the images and the distribution of PCA scores within individual leaf were measured to develop linear discriminant analysis (LDA) models for predicting the infected leaves from healthy leaves. An LDA model classified all the leaves into infected and healthy categories, with an overall accuracy of 92% on validation set. Meanwhile, the classification model base on five selected wavelengths (1188, 1339, 1377, 1432 and 1614 nm) was comparable to that base on the full-spectrum image data.Key words: Rice blast (Pyricularia), Nipponbare, near-infrared hyper-spectral image, principal component analysis, linear discriminant analysis

    Wireless sensor networks for in-situ image validation for water and nutrient management

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    Water and Nitrogen (N) are critical inputs for crop production. Remote sensing data collected from multiple scales, including ground-based, aerial, and satellite, can be used for the formulation of an efficient and cost effective algorithm for the detection of N and water stress. Formulation and validation of such techniques require continuous acquisition of ground based spectral data over the canopy enabling field measurements to coincide exactly with aerial and satellite observations. In this context, a wireless sensor in situ network was developed and this paper describes the results of the first phase of the experiment along with the details of sensor development and instrumentation set up. The sensor network was established based on different spatial sampling strategies and each sensor collected spectral data in seven narrow wavebands (470, 550, 670, 700, 720, 750, 790 nm) critical for monitoring crop growth. Spectral measurements recorded at required intervals (up to 30 seconds) were relayed through a multi-hop wireless network to a base computer at the field site. These data were then accessed by the remote sensing centre computing system through broad band internet. Comparison of the data from the WSN and an industry standard ground based hyperspectral radiometer indicated that there were no significant differences in the spectral measurements for all the wavebands except for 790nm. Combining sensor and wireless technologies provides a robust means of aerial and satellite data calibration and an enhanced understanding of issues of variations in the scale for the effective water and nutrient management in wheat.<br /

    Development and assessment of a multi-sensor platform for precision phenotyping of small grain cereals under field conditions

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    The growing world population, changing food habits especially to increased meat consumption in newly industrialized countries, the growing demand for energy and the climate change pose major challenges for tomorrows agriculture. The agricultural output has to be increased by 70% by 2050 to achieve food and energy security for the future and 90% of this increase must be achieved by increasing yields on existing agricultural land. Achieving this increase in yield is one of the biggest challenges for the global agriculture and requires, among other things, an efficient breeding of new, higher-yielding varieties adapted to the predicted climate change. To achieve this goal, new methods need to be established in plant breeding which include efficient genotyping and phenotyping approaches of crops. Enormous progress has been achieved in the field of genotyping which enables to gain a better understanding of the molecular basis of complex traits. However, phenotyping must be considered as equally important as genomic approaches rely on high quality phenotypic data and as efficient phenotyping enables the identification of superior lines in breeding programs. In contrast to the rapid development of genotyping approaches, phenotyping methods in plant breeding have changed only little in recent decades which is also referred to as phenotyping bottleneck. Due to this discrepancy between available phenotypic and genotypic information a significant potential for crop improvement remains unexploited. The aim of this work was the development and evaluation of a precision phenotyping platform for the non-invasive measurement of crops under field conditions. The developed platform is assembled of a tractor with 80 cm ground clearance, a carrier trailer and a sensor module attached to the carrier trailer. The innovative sensors for plant phenotyping, consisting of several 3D Time-of-Flight cameras, laser distance sensors, light curtains and a spectral imaging camera in the near infrared reflectance (NIR) range, and the entire system technology for data acquisition were fully integrated into the sensor module. To operate the system, software with a graphical user interface has been developed that enables recording of sensor raw data with time- and location information which is the basis of a subsequent sensor and data fusion for trait determination. Data analysis software with a graphical user interface was developed under Matlab. This software applies all created sensor models and algorithms on sensor raw data for parameter extraction, enables the flexible integration of new algorithms into the data analysis pipeline, offers the opportunity to generate and calibrate new sensor fusion models and allows for trait determination. The developed platform facilitates the simultaneous measurement of several plant parameters with a throughput of over 2,000 plots per day. Based on data of the years 2011 and 2012, extensive calibrations were developed for the traits plant height, dry matter content and biomass yield employing triticale as a model species. For this purpose, 600 plots were grown each year and recorded twice with the platform followed by subsequent phenotyping with state-of-the-art methods for reference value generation. The experiments of each year were subdivided into three measurements at different time points to incorporate information of three different developmental stages of the plants into the calibrations. To validate the raw data quality and robustness of the data collection and reduction process, the technical repeatability for all developed data analysis algorithms was determined. In addition to these analyses, the accuracy of the generated calibrations was assessed as the correlations between determined and observed phenotypic values. The calibration of plant height based on light curtain data achieved a technical repeatability of 0.99 and a correlation coefficient of 0.97, the calibration of dry matter content based on spectral imaging data a of 0.98 and a of 0.97. The generation and analysis of dry biomass calibrations revealed that a significant improvement of measurement accuracy can be achieved by a fusion of different sensors and data evaluations. The calibration of dry biomass based on data of the light curtains, laser distance sensors, 3D Time-of-Flight cameras and spectral imaging achieved a of 0.99 and a of 0.92. The achieved excellent results illustrate the suitability of the developed platform, the integrated sensors and the data analysis software to non-invasively measure small grain cereals under field conditions. The high utility of the platform for plant breeding as well as for genomic studies was illustrated by the measurement of a large population with a total of 647 doubled haploid triticale lines derived from four families that were grown in four environments. The phenotypic data was determined based on platform measurements and showed a very high heritability for dry biomass yield. The combination of these phenotypic data with a genomic approach enabled the identification of quantitative trait loci (QTL), i.e., chromosomal regions affecting this trait. Furthermore, the repeated measurements revealed that the accumulation of biomass is controlled by temporal genetic regulation. Taken together, the very high robustness of the system, the excellent calibration results and the high heritability of the phenotypic data determined based on platform measurements demonstrate the utility of the precision phenotyping platform for plant breeding and its enormous potential to widen the phenotyping bottleneck.Die stetig wachsende Weltbevölkerung, sich ändernde Ernährungsgewohnheiten hin zu vermehrtem Fleischkonsum in Schwellenländern, der stetig wachsende Energiebedarf sowie der Klimawandel stellen große Herausforderungen an die Landwirtschaft von morgen. Um eine gesicherte Lebensmittel- und Energieversorgung zu gewährleisten muss die landwirtschaftliche Produktion bis 2050 um 70% gesteigert werden, wobei 90% dieser Steigerung durch eine Erhöhung der Erträge auf bereits bestehenden landwirtschaftlichen Flächen erzielt werden muss. Diese erforderliche Ertragssteigerung ist eine der größten Herausforderungen für die weltweite Landwirtschaft und bedarf unter anderem einer effizienten Züchtung neuer, an den Klimawandel angepasster, ertragsreicherer Sorten. Um eine ausreichende Steigerung der Erträge sicherstellen zu können müssen neue Methoden in der Pflanzenzucht etabliert werden, welche auf einer effizienten Geno- sowie Phänotypisierung der Pflanzen basieren. Im Bereich der Genotypisierung gab es in den letzten Jahrzehnten große Fortschritte, wodurch ein enormer Wissenszuwachs über die molekulare Basis komplexer Merkmale erzielt werden konnte. Trotzdem ist der Bereich der Phänotypisierung als ebenso wichtig anzusehen, da genetische Untersuchungen unter anderem von der Qualität phänotypischer Daten abhängen und qualitativ hochwertige phänotypische Daten die Selektion überlegener Linien in der Pflanzenzucht verbessern können. Im Vergleich zur Genotypisierung gab es jedoch im Bereich der Phänotypisierung in den letzten Jahrzehnten nur wenig wissenschaftlichen Fortschritt. Durch dieses Missverhältnis zwischen der Qualität phänotypischer und genotypischer Informationen bleibt somit ein erhebliches Potential an neuen Erkenntnissen unentdeckt. Das Ziel dieser Arbeit war die Entwicklung und Bewertung einer Präzisionsphänotypisierungsplattform zur zerstörungsfreien Charakterisierung von Energiegetreide in der Pflanzenzucht, um den aktuell bestehenden Flaschenhals bei der Umsetzung neuer Zuchtmethoden zu weiten. Die entwickelte Plattform ist ein Gespann bestehend aus einem Hochradschlepper mit 80 cm Bodenfreiheit, einem eigens entwickelten Trägeranhänger und einem am Trägeranhänger befestigten Sensormodul. Die innovative Sensorik zur Pflanzenvermessung, bestehend aus mehreren 3D Time-of-Flight Kameras, Laserabstandssensoren, Lichtgittern und einem bildgebenden Spektralmessgerät im nahen infrarot (NIR) Bereich, sowie die gesamte Systemtechnik zur Datenaufnahme wurden vollständig im Sensormodul integriert. Zur Bedienung des Systems wurde eine Software mit graphischer Benutzeroberfläche entwickelt, die eine zeit- und ortsbezogene Aufnahme der Sensorrohdaten ermöglicht, was die Grundlage einer anschließenden Sensor- und Datenfusion zur Merkmalsbestimmung darstellt. Zur Datenauswertung wurde eine Software mit graphischer Benutzeroberfläche unter Matlab entwickelt. Durch diese Software werden alle erstellten Sensormodelle und Algorithmen zur Datenauswertung auf die Rohdaten angewendet, wobei neue Algorithmen flexibel in das System eingebunden, Sensorfusionsmodelle erzeugt und kalibriert und Pflanzenparameter bestimmt werden können. Die entwickelte Plattform ermöglicht die simultane Vermessung mehrerer Pflanzenparameter bei einem Durchsatz von über 2000 Parzellen pro Tag. Basierend auf Daten aus den Jahren 2011 und 2012 wurden umfangreiche Kalibrierungen für die Parameter Pflanzenhöhe, Trockensubstanzgehalt und Trockenmasse für Triticale erstellt. Zu diesem Zweck wurden in beiden Jahren Feldversuche mit jeweils 600 Parzellen angelegt, doppelt mit der Plattform vermessen und zur Referenzwertgenerierung im Anschluss konventionell phänotypisiert. In beiden Jahren wurden drei Messungen von jeweils 200 Parzellen zu drei verschiedenen Zeitpunkten durchgeführt, um Daten unterschiedlicher Entwicklungsstadien der Pflanzen für die Erstellung der Kalibrierungen zur Verfügung zu haben. Zur Validierung der Rohdatenqualität sowie der Robustheit der Datenreduktionsverfahren wurden zunächst für alle entwickelten Auswertungsalgorithmen basierend auf den Wiederholungsmessungen die technischen Wiederholbarkeiten bestimmt. Neben der Validierung der Rohdatenqualität wurden die Genauigkeiten der erstellten Kalibrierungen als Korrelation zwischen den Referenzwerten und den mit der Sensorplattform gemessenen Werten ermittelt. Die Kalibrierung der Pflanzenhöhe basierend auf Lichtgitterdaten erreicht eine technische Wiederholbarkeit Rw2 von 0.99 und einen Korrelationskoeffizienten Rc² von 0.97, die Kalibrierung des Trockensubstanzgehalts basierend auf Spectral-Imaging Daten ein Rw2 von 0.98 und ein Rc² von 0.97. Bei der Erstellung der Trockenmasse Kalibrierung konnte gezeigt werden, dass durch eine Fusion verschiedener Sensoren und Datenauswertungen eine signifikante Verbesserung der Messgenauigkeit erreicht werden kann. Die Kalibrierung der Trockenmasse basierend auf Daten der Lichtgitter, Laserabstandssensoren, 3D Time-of-Flight Kameras und des Spectral-Imaging erreicht ein Rw2 von 0.99 und ein Rc² von 0.92. Die hervorragenden technischen Wiederholbarkeiten, sowie die exzellenten Genauigkeiten der entwickelten Kalibrierungen verdeutlichen die herausragende Eignung der entwickelten Plattform, der integrierten Sensoren und der entwickelten Datenaufnahme- sowie Datenauswertesoftware zur zerstörungsfreien Phänotypisierung von Getreide unter Feldbedingungen. Der hohe praktische Nutzen der Plattform für die Pflanzenzucht sowie für genetische Studien konnte durch die wiederholte Phänotypisierung einer DH Population mit 647 doppelhaploiden Triticale Linien in vier Umwelten aufgezeigt werden. Die Pflanzen wurden mit der Plattform an drei verschiedenen Zeitpunkten phänotypisiert und die erzeugten Daten zeigten eine sehr hohe Heritabilität für Biomasse. Die Kombination dieser phänotypischen mit genotypischen Informationen in einer Assoziationskartierungsstudie ermöglichte die Identifizierung von Regionen im Genom welche für quantitative Merkmale (QTL) kodieren. So konnten z.B. Regionen auf mehreren Chromosomen identifiziert werden, welche die Biomasse beeinflussen. Des Weiteren konnte durch Auswertung der wiederholten Messungen der Nachweis erbracht werden, dass die Biomasseentwicklung durch sich zeitlich ändernde genetische Mechanismen beeinflusst wird. Die erreichte sehr hohe Robustheit des Systems, die exzellenten Kalibrierungsergebnisse und die hohen Heritabilitäten der mit der Plattform bestimmten phänotypischen Daten verdeutlichen die hervorragende Eignung des Systems zur Anwendung in der Pflanzenzucht und das enorme Potential der entwickelten Technologie zur Weitung des aktuell bestehenden Phänotypisierungs-Flaschenhalses

    Optimising configuration of a hyperspectral imager for on-line field measurement of wheat canopy

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    There is a lack of information on optimal measurement configuration of hyperspectral imagers for on-line measurement of a wheat canopy. This paper aims at identifying this configuration using a passive sensor (400–750 nm). The individual and interaction effects of camera height and angle, sensor integration time and light source distance and height on the spectra's signal-to-noise ratio (SNR) were evaluated under laboratory scanning conditions, from which an optimal configuration was defined and tested under on-line field measurement conditions. The influences of soil total nitrogen (TN) and moisture content (MC) measured with an on-line visible and near infrared (vis-NIR) spectroscopy sensor on SNR were also studied. Analysis of variance and principal component analysis (PCA) were applied to understand the effects of the laboratory considered factors and to identify the most influencing components on SNR. Results showed that integration time and camera height and angle are highly influential factors affecting SNR. Among integration times of 10, 20 and 50 ms, the highest SNR was obtained with 1.2 m, 1.2 m and 10° values of light height, light distance and camera angle, respectively. The optimum integration time for on-line field measurement was 50 ms, obtained at an optimal camera height of 0.3 m. On-line measured soil TN and MC were found to have significant effects on the SNR with Kappa values of 0.56 and 0.75, respectively. In conclusion, an optimal configuration for a tractor mounted hyperspectral imager was established for the best quality of on-line spectra collected for wheat canopy

    Hyperspectral measurements of yellow rust and fasarium head blight in cereal crops: Part 2: On-line field measurement

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    Yellow rust and fusarium head blight cause significant losses in wheat and barley yields. Mapping the spatial distribution of these two fungal diseases at high sampling resolution is essential for variable rate fungicide application (in case of yellow rust) and selective harvest (in case of fusarium head blight). This study implemented a hyperspectral line imager (spectrograph) for on-line measurement of these diseases in wheat and barley in four fields in Bedfordshire, the UK. The % coverage was assessed based on two methods, namely, infield visual assessment (IVA) and photo interpretation assessment (PIA) based on 100-point grid overlaid RGB images. The spectral data and disease assessments were subjected to partial least squares regression (PLSR) analyses with leave-one-out cross-validation. Results showed that both diseases can be measured with similar accuracy, and that the performance is better in wheat, as compared to barley. For fusarium, it was found that PIA analysis was more accurate than IVA. The prediction accuracy obtained with PIA was classified as good to moderately accurate, since residual prediction deviation (RPD) values were 2.27 for wheat and 1.56 for barley, and R2 values were 0.82 and 0.61, respectively. Similar results were obtained for yellow rust but with IVA, where model performance was classified as moderately accurate in barley (RPD = 1.67, R2 = 0.72) and good in wheat (RPD = 2.19, R2 = 0.78). It is recommended to adopt the proposed approach to map yellow rust and fusarium head blight in wheat and barley
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