Final report SRDC project BSS295 Scoping study - remote sensing of sugarcane leaf diseases

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1 Sugar Research Australia Ltd. elibrary Completed projects final reports Pest, Disease and Weed Management 2006 Final report SRDC project BSS295 Scoping study - remote sensing of sugarcane leaf diseases Magarey, RC Downloaded from Sugar Research Australia Ltd elibrary

2 BSES Limited FINAL REPORT SRDC PROJECT BSS295 SCOPING STUDY REMOTE SENSING OF SUGARCANE LEAF DISEASES by RC MAGAREY SD06004 Contact: Dr Rob Magarey Principal Research Scientist BSES Limited PO Box 566 Tully Q 4854 Telephone: Facsimile: rmagarey@bses.org.au BSES is not a partner, joint venturer, employee or agent of SRDC and has no authority to legally bind SRDC, in any publication of substantive details or results of this Project. BSES Limited Publication SRDC Final Report SD06004 April 2006

3 Copyright 2006 by BSES Limited All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of BSES Limited. Warning: Our tests, inspections and recommendations should not be relied on without further, independent inquiries. They may not be accurate, complete or applicable for your particular needs for many reasons, including (for example) BSES Limited being unaware of other matters relevant to individual crops, the analysis of unrepresentative samples or the influence of environmental, managerial or other factors on production. Disclaimer: Except as required by law and only to the extent so required, none of BSES Limited, its directors, officers or agents makes any representation or warranty, express or implied, as to, or shall in any way be liable (including liability in negligence) directly or indirectly for any loss, damages, costs, expenses or reliance arising out of or in connection with, the accuracy, currency, completeness or balance of (or otherwise), or any errors in or omissions from, any test results, recommendations statements or other information provided to you.

4 CONTENTS Page No SUMMARY...i 1.0 BACKGROUND OBJECTIVES MEETINGS TO DISCUSS THE PROPOSED RESEARCH BACKGROUND TO REMOTE SENSING TECHNOLOGY Transport Detection technologies Satellite based technologies Airborne images TECHNOLOGY RECOMMENDATIONS FOR SUGARCANE BACKGROUND RESEARCH ON ORANGE RUST FUTURE RESEARCH Characterising disease spectral emissions (1 year duration) Application of remote sensing to the field (2 years duration) INDICATIVE BUDGET INDUSTRY INVOLVEMENT ACKNOWLEDGMENTS REFERENCES...9 APPENDIX 1 Paper by Apan et al...10

5 SUMMARY Estimation of the incidence of sugarcane leaf diseases in the Australian sugar industry is largely confined to subjective estimates by Cane Productivity Services personnel. Resistance ratings for leaf diseases are applied to clones in the plant improvement program through intense in field disease assessments of on farm trials. Both methods have problems either the inability to objectively measure diseases over large areas, or the very high time input required to capture the data. Remote sensing of leaf diseases offers the possibility of obtaining objective disease incidence data, both on a regional basis, but also for individual crops. Small plots in field trials also could be assessed for disease resistance through remote sensing techniques. Consultation with researchers in ENSIS and Mackay Sugar centred on the remote sensing technologies available in Australia. The one considered most suitable for initial studies is the Hyperion hyperspectral technology. This has been used previously to detect orange rust in sugarcane crops in the Mackay area. The involvement of Hyperion in initial studies would also allow the simulation of other types of remote sensing technology, such as SPOT IV, Landsat, and other multispectral detection capabilities. A three year research program was devised that would initially test the technology for remote sensing of leaf diseases. The adaptation of these techniques to a whole district would follow in the two subsequent years. The Herbert River district was selected as a model district, since all three major leaf diseases occur in this area (yellow spot, orange rust and brown rust) and there is a greater likelihood of obtaining satellite images in this area compared to the wet tropics (Tully). Following the successful development of the remote sensing technology, project work would involve milling and grower sectors of the industry. There would be a concentration on the uses and delivery of the crop and industry disease information, as presented in a GIS format. At the same time, research on high resolution imagery of small plots in field trials would lead to methods for assigning disease resistance ratings for clones in plant improvement trials. This work will follow naturally on from the technology development associated with whole crop disease detection. Researchers from both Mackay Sugar and ENSIS agree that sugarcane is a very suitable crop for remote sensing technology, with a high chance of successful disease detection.

6 1.0 BACKGROUND Remote sensing refers to the distant detection of specific crop characteristics by various detection mechanisms, usually related to the emission of certain frequencies of electromagnetic radiation an example are wavelengths in the near infrared (NIR) region. Remote sensing of leaf diseases would have the advantage of decreasing labour inputs into disease assessment at the ground level, and thus raising the possibility of assessing disease incidence over vast cropping areas. The disease data obtained can be easily transferred to a GIS type information base that allows specific information to be communicated to individual farmers, industry stakeholders and/or research bodies. Interest in the subject was raised by a poster at the 15th Biennial Australasian Plant Pathology Society conference at Geelong, Victoria in October The poster paper titled From leaf to landscape, identifying Mycosphaerella leaf blight in Eucalyptus globulus plantations using digital multi spectral imagery (by E. Pietrzykowski et al.) described research where the canopy of some northern Tasmanian forests was being monitored for Mycosphaerella leaf blight using visible and near infrared reflectance techniques. Maps were created and forest health assessed using aircraft mounted detection equipment. This raised the possibility that similar technologies in sugarcane could provide the following: Objective assessment of leaf disease incidence across regions: this is currently guess timated by Cane Productivity Service staff on a subjective basis. A remote sensing system could provide objective data over the whole cropping area. Disease incidence in individual crops: using GIS systems, there could be potential to report to farmers on leaf disease incidence in individual crops a big step forward compared to the current situation. Disease resistance ratings: by using high resolution equipment, there is potential to remotely sense disease incidence in small plots in plant improvement trials, so that leaf disease resistance ratings could be applied to advanced clones. This would greatly improve the ability to assess the resistance of families and clones, and assist breeders in clonal selection and disease resistance strategies. The Sugar Research and Development Corporation (SRDC) and BSES Limited provided funds for scoping of the potential for remote sensing techniques to meet the three suggested uses in the sugarcane industry related to disease assessment. This report details information gathered from meeting with research staff with experience in remote sensing, and with a sugarcane industry group interested in the application of the technology. 2.0 OBJECTIVES The project aimed to: 1. Assess whether equipment used by the ENSIS team for detection of forest canopy diseases is suitable for detecting sugarcane leaf diseases; 2. Discuss with the ENSIS team the development of a research plan for sugarcane leafdisease assessment;

7 2 3. Assess the chances of success for clone resistance assessment in plant improvement trials; 4. Assess the possibility of remote district disease severity assessments linked to GIS systems; and 5. Estimate the likely cost of the work. All five objectives were achieved as described below. 3.0 MEETINGS TO DISCUSS THE PROPOSED RESEARCH Two meetings were held to discuss the proposed research: Melbourne: Monday 6 March: Participants: Dr Neil Simms, ENSIS; Dr Rob Magarey, BSES Limited. The meeting was held at Ensis, Clayton, Melbourne, Victoria. At this meeting, discussions centred on the types of remote sensing technologies that could be applicable to the sugar industry. Dr Simms outlined the relevant activities undertaken by ENSIS. He spoke of the remote detection of diseases in forest canopies, assessing insect incidence in pine plantations, the assessment of nitrogen content in forest plantations and species identification in forests. Examples of leaves affected by the three major sugarcane leaf diseases were shown to ENSIS staff; it was determined that using such material for emission characterisation was not possible during this visit. Such assessments would best be undertaken in the first part of any follow up project activities. At the meeting, it was decided that Dr Simms should visit Queensland to gain first hand knowledge of the sugarcane crop architecture, and to view diseases in the field. Cairns: Thursday, 20 April: Participants: Dr Neil Simms, ENSIS; Mr John Markley, Mackay Sugar, and Dr Rob Magarey, BSES Limited. The meeting was held at BSES Meringa rather than in Mackay for several reasons travel into Cairns was easier for Dr Simms, and more varieties and diseases were present at BSES Meringa. At this meeting, Dr Simms was able to view the sugarcane crop first hand, to confirm how remote sensing technologies may apply to sugarcane. Mr Markley has experience in the application of remote sensing techniques to the detection of orange rust in the Mackay area, and his input was related to technology application and the types of detection techniques that may be more applicable to the various diseases. With Mr Markley present, we were able to discuss in more depth the suitability of various technologies.

8 3 4.0 BACKGROUND TO REMOTE SENSING TECHNOLOGY Discussions with Dr Simms and Mr Markley provided useful information related to remote sensing technologies. These are detailed below. A number of techniques are used in remote sensing activities where the emission of electromagnetic radiation is the primary sensing technique. The first categorisation applies to the type of transport to which the detection equipment is attached. 4.1 Transport Aircraft mounted technology: some users utilise aircraft or balloon mounted detection instruments (such as in the eucalypt forest case described above). This can provide highresolution information, but can also be very expensive for assessing large cropping areas. Satellite mounted instruments: with considerable interest in remote sensing in a vast array of situations, both within and between countries, there are many technologies linked to currently orbiting satellites. Some of these are more cost effective for assessing disease incidence over larger areas. 4.2 Detection technologies Disease detection relies on the sensing of specific crop electromagnetic emissions that can be linked to the particular disease in question. These are referred to as Spectral Vegetation Indices (SVIs). Unique SVIs for diseases can be obtained by analysing the spectral emissions (characteristics of the electromagnetic radiation reflected from the vegetation) emanating from vegetation, and selecting those associated with the presence of the disease. In the Melbourne based ENSIS group, various detection technologies have been used to assess such things as disease in forest canopies, insect pests in pine plantations, nitrogen content of pine plantations and species identification in forests. Different technologies proved better under each individual circumstance. The detection technologies that may be suitable are summarised below Satellite based technologies Hyperion: a satellite based hyper spectral system able to detect wavelengths in the broad visible and near infrared ranges, with detection of wavelengths to 2 nm band widths. A more or less continuous detection range provides excellent opportunity to develop specific disease related emissions. The technology has a pixel size of 30 m x 30 m, providing medium resolution of district images. Image costs are around $1,000. Current satellites are able to provide the images. Problems with Hyperion include the fact that the space agency providing the service, (NASA), has advised that the satellite EO 1 is due to be terminated sometime in the short to medium term. Hyperion technology also needs to be tasked, ie images in the specific wavelengths specific for disease detection need to

9 4 requested prior to image capture there are no historical images available. The advantage is the hyper spectral image obtained a broad range of wavelengths are detected with small band widths (2 nm). Landsat: a satellite based system, two satellites are currently in orbit. The technology is multi spectral broad bands are detected (rather than narrow bands right across the range of wavelengths). Wavelengths detected are in the visible, NIR and SWIR regions. Landsat 5: has detection equipment that could be suitable, but the satellite is nearing the end of its life. A system based on this satellite is unlikely to be suitable in the longer term. Landsat 7: is younger, but unfortunately has a problem in the images taken; the images are incomplete. This limits the use of this satellite, and so it would be unwise to use the equipment mounted on Landsat 7. Spot IV: this technique involves the measurement of wavelengths in four broad bands only, across the visible (green and red), near infrared and short wave ranges. The costs are cheaper, but there is more limited ability to detect specific wavelengths related to individual diseases. There are good spatial detection abilities (across regions for example); pixel size is 10 m x 10 m. Aster: also attached to the Japanese research satellite EO 1, Aster technology is multispectral with 11 bands. It has the advantage over Landsat 5 and 7 and Spot IV of better resolution in the visible and NIR bandwidths (15 m pixel size), plus it has six bands of shortwave infra red (SWIR) available. Images are good quality, but the imagery is unusual in that it has different pixel sizes in each wavelength, ranging up to 90 m in the SWIR. ASTER has a larger scene area than Hyperion. However, it has similar problems to Hyperion, in that the imagery needs to be tasked (rather than continuous collection) and that the EO 1 satellite, on which both Hyperion and ASTER sensors reside, is due to be terminated in the not too distant future. It also may be more expensive than Hyperion, because there of a special offer on free data acquisition for Hyperion. Quickbird: is a high resolution satellite imagery of higher cost than Hyperion. It is multi spectral, with 2.4 m x 2.4 m pixel size. There is also a facility for higher resolution in pan chromatic wavelengths (equivalent to the concept of black and white photographs) with pixel size of 0.60 m x 0.60 m. Minimum area of the images is around 25 km 2. Costs are unknown at this time Airborne images Digital multi spectral cameras: For more detailed analysis of smaller plots, digital multi spectral cameras can be flown on light aircraft or other airborne suspension mechanisms. Commercial companies, such as Specterra, operate such services in Australia. There are four adjustable detectable bands that can be targeted as needed to sense individual diseases. Costs are around $1/ha; this would make the costs prohibitive to whole district analysis, but individual field trials could be assessed quite economically. Generally visible or NIR bands are captured using this technology.

10 5 5.0 TECHNOLOGY RECOMMENDATIONS FOR SUGARCANE Of the technologies outlined above, Dr Simms and Mr Markley agreed that Hyperion detection technologies would be more likely to provide the optimum starting point in any future sugarcane disease detection research. The reasons for this assessment are: Hyperion technology has the appropriate resolution needed; Background research and experience with orange rust has been undertaken using Hyperion; Hyperion images can be obtained at reasonable cost; Hyperion images can be used to simulate multi spectral image capture (and so assess how the other multi spectral images may perform) by selecting more specific band widths from the hyper spectral image. We undertook no detection testing of sugarcane leaf diseases within this project, as it was agreed that the first stage of any further work should address this objective. However, Dr Simms is pursuing previous researchers involved in the orange rust work, to see what other sugarcane canopy parameters they studied. Mr Markley suggested a couple of other diseases may have been considered briefly. 6.0 BACKGROUND RESEARCH ON ORANGE RUST Previous research on remote sensing for sugarcane leaf diseases is restricted to orange rust in central Queensland. Researchers investigating this came from the University of Southern Queensland, The University of Queensland, CSIRO, and Mackay Sugar. The focus was on developing Spectral Vegetation Indices (SVIs) for sugarcane orange rust using the E0 1 satellite and Hyperion imagery. Image processing before analysis included correction for atmospheric effects, selection of the appropriate radiation bands, destreaking, and removal of bad pixels. Several other transformations of the data were applied. SVIs were selected that focussed on one or more of the following crop attributes leaf pigments, internal leaf structure and canopy moisture levels. The method is described by Apan et al. (2003) (Appendix 1). With the selection of specific SVIs, satellite images were linked to field disease incidence through inspection by field staff of disease incidence in selected crops. The best discrimination between healthy and diseased crops occurred in the NIR region ( nm and in 1070 nm). Lower discrimination occurred in the SWIR (separability peaked between 1660 and 2200 nm), green (550 nm) and red (680 nm) bands. Discrimination function analysis showed that finding the ratio between the 1600 nm SWIR band, with either the NIR band (800 nm) or green band (550 nm), produced the best correlation with disease incidence. A 96.9% classification accuracy was obtained using this index.

11 6 7.0 FUTURE RESEARCH We concluded that further research be broken into two projects, or alternatively, separate components of a single project. 7.1 Characterising disease spectral emissions (1 year duration) Hyperion technology would be used to determine if each major leaf disease could be characterised by signature SIVs. Some laboratory work may be involved, but essentially this would involve use of Hyperion imagery from satellite based equipment coupled with field assessment of the targeted crops. The correlation of the remote sensing data with the field disease severity information would be used to determine the success of the formulated SIVs. Hyperion technology will allow the simulation of the detection possibilities with multi spectral technology; this would be tested in the one year initial research phase. As research with orange rust has already been undertaken, the first disease considered would be orange rust. Others to be included are yellow spot and brown rust. A 1 year project (or section of a larger project) was considered adequate for this work. 7.2 Application of remote sensing to the field (2 years duration) After discussions, it was suggested that the Herbert River district, rather than Tully or Mackay, would be the best area to do further research; the Herbert has more cloud free days than Tully, allowing successful satellite imagery. Herbert crops also are affected by all three major leaf diseases (whereas Mackay is not), and there is a good GIS information base in the Herbert district (the Herbert Resource Information Service). The detailed research would be split over 2 years; in year one, the process would be developed, whilst in year two, consistency of the results would be confirmed. In the second year, the application of the remote sensing capability to small field plots would be investigated. The same basic steps would apply as listed below for whole crop disease detection; the actual remote sensing technology used will depend in part on the type of SVIs developed for each disease. Steps in the proposed research are: 1. Identify exact field study areas this may require a visit by an Ensis remote sensing scientist to the Tully Ingham region. 2. Identify varieties and age classes into which the study area will be stratified. 3. Acquire all possible ancillary data for the cropping areas, including shapefiles or vector layers delineating and identifying sugarcane blocks, digital elevation and image data, and field data describing the distribution and severity of the leaf diseases of interest. 4. Task capture of Hyperion imagery over the study area. Tasking image capture to commence in August to detect brown rust, and continue until March in the following year to capture images in the wet season peak expression period, or until sufficient high quality images have been acquired. Multiple scenes may be required to cover the study area

12 7 5. Task the capture of high resolution satellite imagery (possibly Ikonos or Quickbird). In addition to providing increased spatial information overall, this may be used to test the suitability of multi spectral imagery for detecting and quantifying the diseases, especially in the resistance trial plots which may be as small as 10 m by 6 m and not identifiable in the Hyperion images (30 m pixels). 6. Contact BSES staff upon confirmation of cloud free image capture. Field teams should then assess disease levels in designated crops as soon as possible after image capture, (within 2 weeks of image capture), to ensure that accurate models can be developed from the images. Field data requirements include: GPS location collected in MGA, GDA94 coordinates; Type, distribution and severity of disease; Variety, crop class and resistance rating (if known); Canopy cover characteristics of the sugarcane (percent cover); Soil colour descriptions (Munsell colour numbers if possible). 7. Field data should be collected from a minimum of 100 sampling sites throughout the study area. Sites should be distributed in association with the proportions of crop classes and varieties into which the study is stratified. 8. Multiple field sites per block (four sites) will be required in at least three blocks per strata to validate the within block estimation of disease severity and distribution. 9. Digital photographs of the study sites may be useful in interpreting results 10. Acquire (task if necessary) large scale multispectral image data if initial analysis of the high resolution imagery indicates that useful and robust models can be created from multispectral data 11. Image processing will include: Spectral correction of the images to minimise the influence of solar, atmospheric and sensor artefacts on image data Spatial registration of the images to spatially register them to one another and rectify them to a map grid Modelling procedures may include transformation of the reflectance data and a range of regression modelling techniques including multiple and partial least squares algorithms. 12. It may be useful to examine the robustness of the models developed in this project for monitoring purposes. This could be achieved using archived imagery, provided that suitable historical field data is available around the time of image capture. Alternatively, image capture could be tasked for the subsequent growing season and field data collected specifically to validate the models.

13 8 7.0 INDICATIVE BUDGET Year 1: Organisation Cost ($) ENSIS: Including satellite data capture, image processing, travel 65,000 and other consumables Mackay Sugar: including travel, consumables, and data processing 6,000 BSES Limited: including supply of disease specimens, assessment of 10,000 of disease in the field Herbert Resource Information Service: supply of GIS information 5,000 related to the Herbert. Total $86,000 Years 2 and 3: Organisation Cost($) / year ENSIS: Including satellite data capture, image processing, travel 80,000 and other consumables Mackay Sugar: including travel, consumables, and data processing 10,000 BSES Limited: grower group funding, assessment of leaf diseases, 25,000 and data processing. CSR Limited: data processing, GIS information 5,000 Herbert Resource Information Service: supply of GIS information 10,000 related to the Herbert. Total per year $130, INDUSTRY INVOLVEMENT The proposed research would intimately involve milling, growing and extension sectors of the industry. Mackay Sugar and CSR Limited would provide input into milling aspects including identification of individual crops on farms, and the reporting back to farmers of information on disease incidence. Grower groups would be involved in the development of the proposed remote sensing capability through assessment of benefits to farmers and other applications of the data/techniques. This would especially apply to how the diseases are reported to industry organisations and individual farmers. Details of their involvement will be more fully explained in any follow up project proposal. 9.0 ACKNOWLEDGMENTS I thank SRDC and BSES for both the financial support and encouragement to undertake this study tour. I also thank Dr Neil Simms and Mr Jon Markley for their valuable time inputs.

14 REFERENCES Apan, A., Held, A., Phinn, S. and Markley, J. (2004) Detecting sugarcane 'orange rust' disease using EO 1 Hyperion hyperspectral imagery. International Journal of Remote Sensing 25: Galvao, L. S., Formaggio, A. R. and Tisot, D. A. (2005) Discrimination of sugarcane varieties in Southeastern Brazil with EO 1 Hyperion data. Remote Sensing of Environment 94: Pietryzkowski, E., Stone, C., Pinkard, E., Simms, N., and Mohammed, C. (2005). From leaf to landscape identifying Mycosphaerella leaf blight in Eucalyptus globulus plantations using digital multispectral imagery. 15 th Biennial Australasian Plant Pathology Society Conference Handbook, p. 138.