REMOTE SENSING ON GANODERMA DISEASE

Transcription

1 REMOTE SENSING ON GANODERMA DISEASE Nisfariza Mohd Noor Maris Senior Lecturer Department of Geography, University of Malaya KUALA LUMPUR, MALAYSIA

2 Outline Introduc.on Ganoderma BSR Pathological symptom Detec.on of Ganoderma BSR Sensor for Disease Detec.on Remote Sensing Proper.es of Plant Remote Sensing Research on Ganoderma Disease in MPOB GER1500 Spectroradiometer Data & Analysis AISA Hyperspectral Data & Analysis Unmanned Aerial Vehicle Data & Analysis Integra.on of GIS and Remote Sensing Future Developments Conclusion and Recommenda.on 2

3 Introduction Oil palm (Elaeis guineensis Jacq.) the most important agriculture sector in Malaysia Damage and loss due to pests and diseases : serious problems and can reduce the profitability of the business; jeopardise the growth of the industry over Dme. Three major oil palm threats for the crop losses can be classified into insects, vertebrates and diseases. 3

4 Cont Monitoring agricultural areas : Dme and labour consuming dealing with large area of homogenous crop. Disease and pest detecdon and control are important phases in agricultural management. Cultural pracdces combined with biological and chemical control have been considered as the best approach for controlling diseases and pests. Remote sensing provides a possible soludon to the intensive sampling required for site- specific pest management. 4

5 Ganoderma BSR Fatal disease most serious disease of oil palm in Malaysia for over 80; severe economic loss in Malaysia. The soil- borne fungi anack from the roots and progressively spread to the stem and bole system. 5 Prevents water and nutrient uptake : foliar symptoms and gradually affects the growth and yield, eventually leading to the death of the palm

6 Detection of Ganoderma BSR Visual interpretadon - middle or late stages of disease pathology commonly Dme- consuming, destrucdve and expensive. Aerial detecdon enable treatment at the early stage of the infecdon and avoid more extensive damage and losses. Wet lab detecdon - subclinical symptom of Ganoderma GSM test, PCR- DNA molecular, ELISA- polyclonal andbody and GanoScan1 tomography. 6

7 Pathological Symptom The visible symptom of is seen at later stage: infesta.on of the pathogen has damaged the stem and bole of the oil palm. 7 Four Ganoderma species to be associated with oil palm in Malaysia: G. boninense, G. zonatum, G. miniatocinctum and G. tornatum but G. boninense is the most aggressive (Idris, 1999).

8 8... Cont

9 Sensors for disease and stress detec.on in plants Molecular methods Bio- chemical analysis Biomarker- based sensor Remote Sensing Remote Sensing Spectroscopic Techniques Imaging Techniques Visible- near infrared spectrosc opy Fluoresce nce spectrosc opy Mid infrared spectrosc opy Fluoresce nce imaging Hyperspe ctral imaging Mul.spec tral Imaging RADAR Others 9 Modified from Reza Ehsani (2010)

10 Remote Sensing Properties of Plant Electromagnetic Spectrum (Light + ) incoming light is preferentially absorbed (reflected) depending on plant physiology Species Photosynthesis Water Content

11 11 Elowitz h8p://

12 Biochemical Response of Leaf Philpot,

13 Remote Sensing Research on Ganoderma in MPOB Spectroradiometer (GER1500) Hyperspectral (AISA DUAL) Multispectral (UAV Swinglet) RADAR 13

14 Hyperspectral Approach Spectroradiometry Hyperspectral Imaging Nursery (seedlings) Planta.on Planta.on 10 months old 5 years old young palm 5 years old young palm 24 months old 17 years old mature palms 17 years old mature palms

15 Visual Nursery trials Severity Classifica.on Visual Assessments T1 Seedlings not inoculated and not infected with Ganoderma boninense, seedling is healthy. Leaves and tree looking healthy. Absence of white mycelium or fruidng body (Ganoderma) at stem base. T2 Seedlings inoculated and infected with Ganoderma boninense, WITHOUT foliar symptoms but with white mycelium or fruidng body at stem base. Leaves and tree looking healthy without any foliar symptom of BSR disease. Presence of white mycelium or fruidng body (Ganoderma) at stem base. 15 T3 Seedlings inoculated and infected with Ganoderma boninense, WITH foliar symptoms and with white mycelium or fruidng body at stem base. Yellowing, browning or drying of some leaves due to Ganoderma infecdon. One or two new leaves remain as unopened spears. Decline of older leaves. Presence of white mycelium or fruidng body (Ganoderma) at stem base.

16 Visual Nursery Trials T1 T2 T3

17 Visual Plantation plots Severity Classifica.on Visual Assessments T1 Healthy palm. Leaves and tree looking healthy. Absence of white mycelium or fruidng body (Ganoderma) at base stem T2 Palm infected with Ganoderma spp. w/o any foliar symptoms but w white mycelium or fruidng body at base stem. Leaves and tree looking healthy. Presence of white mycelium or fruidng body (Ganoderma) at base stem 17 T3 Palm infected with Ganoderma spp. with foliar symptoms and white mycelium or fruidng body at base stem. Yellowing or drying of some leaves. One or two new leaves remain as unopened spears. DeclinaDon of older leaves. Presence of white mycelium or fruidng body (Ganoderma) at base stem.

18 Visual Plantation Plots T1 T2 T3 Visual appearance of severity classes in oil palm plantation for Ganoderma BSR study. 18

19 Data Acquisition & Equipment

20 GER1500 Handheld Spectroradiometer Baseline study of possibility the use of remote sensing in detecting Ganoderma BSR. Massive ground works. Wavelength 400nm to 1050nm. 1.5 nm intervals 512 bands 20

21 Spectroradiometer Data Spectral data processing: Original Reflectance Stats - Original Ref Denoising 5pt smoothing Stats Denoise spectra Compare Statistically First Derivative Stats 1st Derivative 21

22 Raw Reflectance Data T2 Inoculated no foliar symptom but with white mycelium or fruiting body at base stem T1 Inoculated looking healthy, no foliar, mycelium or fruiting body T3 Inoculated with foliar symptom and white mycelium or fruiting body at base stem 22

23 First Derivative Analysis T2 Inoculated no foliar symptom but with white mycelium or fruiting body at base stem SFS = 14% T1 Inoculated looking healthy, no foliar, mycelium or fruiting body SFS = 9% T3 Inoculated with foliar symptom and white mycelium or fruiting body at base stem SFS=49% 23 EXPERIMENT 1

24 Red Edge Position Analysis 24 The inflection point on the slope between the red absorption and near infrared reflectance - used to correlate chlorophyll content with reflectance. Decreasing chlorophyll causes the red edge position to shift towards shorter wavelengths. Shifts in the wavelength position of the red edge and the intensity of the peak slope have been related to stress factors in vegetation such as chlorophyll decline and senescence. The red edge feature is therefore an early indicator of disease symptoms in leaves. 24

25 AISA Dual Hyperspectral System A scale up study from ground to aerial data. Wavelength 400nm to 2500 nm 5nm interval 244 bands 0.68m resolution Specim, Finland. 25

26 26 Data Acquisition and Equipment

27 Hyperspectral Airborne Image Processing Cross Track Illumination Raw Data Processed Data MNF Transform Pre- Processing Radiometric Geometric Atmospheric Spectra Statistics Continuum Removed Vegetation Indices Classification 27 Accuracy Assessment

28 Study Area Planted Palm / Airborne Oil palm plantation in Seberang Perak, Perak, Malaysia (4 6 N E). Data acquired October, 2010 Mature palm 17 years old Young palm 5 years old 28

29 AISA Hyperspectral Image 29 29

30 30 The hyperspectral image acquired 20 October 2008, effect of fluctuations in solar illumination during flight. [Unfavourable weather in October, the month for monsoon transition period in the country].

31 Best Band Selection Significant bands to discriminate palms infected with Ganoderma BSR, it shows that early detection is possible using derivative spectral distinction. Wavelengths bands suitable for discriminating different levels of Ganoderma; the original reflectance spectra and first derivative spectra to obtain the best wavelengths in discriminating the disease. The results showed that for Ganoderma, the discriminating bands for mature and immature oil palms were found in ranges of nm, nm, nm, nm, nm and nm respectively. 31

32 Hyperspectral Vegetation Indices Vegetation Indices indicate the physiological and pathological parameters of vegetation interpret the status of vigour of the plants. It is not always possible to detect disease or other stresses in crops from reflectance values. A disease may be difficult to detect at an early stage, e.g. fungi that attack the roots of plants or the base of their stem, but do not at first cause any reaction in the plants itself. Several indices was tested: the Normalised Difference Vegetation Index (NDVI), Modified Chlorophyll Absorption Ratio Index (MCARI), Structural Independent Pigment Index (SIPI), Optimised Soil Vegetation Index (OSAVI) and Transformed Chlorophyll Absorption in Reflectance Index (TCARI). 32

33 Normalised Difference Vegetation Index (NDVI) 33 Brighter - Healthier

34 Cont 34 NDVI Values Non Vege / Bare Soil : (Red) T1 : (Green) T2: (Yellow) T3: (Blue) 34 Classified / reconciliation based on the index value of NDVI

35 Unmanned Aerial Vehicle (UAV) UAV has become commercially available nowadays have the advantages of high spatial and temporal resolution characteristics compared to airborne and satellite platforms. The advancement of remote sensing technology in providing a synoptic view provides possibility to monitor a large plantation state of vigorousness and stress due to disease, pest and nutrient deficiency. UAV promotes the remote sensing technology in detection and monitoring of existing plot infected with Ganoderma BSR. 35

36 Unmanned Aerial Vehicle Same visual detection concept is applied to the RGB image; since it corresponds to the optical concept of a human s eye. The use of NIR cameras in the UAV is additional whereby it implies the high reflectance of vegetation in the NIR region invisible to the human eye to demonstrate the health of the plant. The RGB and NIR images are coupled with GIS analysis to achieve the aim to monitor Ganoderma BSR in oil palm plantation. 36

37 37 Data Acquisition and Equipment

38 38 Flight Planning

39 UAV Swinglet Data Resolu.on : RGB (10cm) NIR (4cm) 39 The RGB and NIR images.

40 40 Integration of GIS and Remote Sensing

41 GIS Spatial - Temporal Analysis To incorporate all data collected by remote sensing with the census to develop a time series of disease spread over time. Multi sensor and multi temporal. 41

42 What is next? Ganoderma Workshop 2014 (MPOB and Malaysian Oil Palm Stakeholders) : Early detecdon tools more sensidve, precise and localize Standardize the methods and classificadon visualize υfaster, cheaper and precise remote sensing and GIS Aerial detecdon need esdmadon for the stadsdcal analysis using hyperspectral remote sensing. Estate experience 1 UAV for 500 ha per day, depend on certain limitadon

43 Future Developments Develop specific vegetation index for Ganoderma based on significant bands. Modeling on the relationship of Ganoderma disease with factors such as soil type, moisture content, nutrient. Index mapping of Ganoderma disease for areal based identification for management and planning purposes. New UAV hyperspectral system using band selection. GIS mapping, hotspots and prediction of disease dispersion. RADAR (Radio Detection and Ranging) fusion with hyperspectral imagery. 43

44 Conclusion and Recommendation Identification based on percentage over total area and assessment of the disease symptoms Early detection of the disease is vital for profitable agriculture business. Plantation management: early identification ameliorate on plan and strategy to overcome the problem in a cost effective manner. 44 Expands the Ganoderma BSR detection survey options available to plantation managers and a viable data source for detecting and monitoring damage due to pest and disease in oil palm plantation. Cheaper technology for detection

45 Acknowledgement This research is funded by: Innovative Technology Research Cluster (ITRC), University of Malaya Ganoderma and Disease Research of Oil Palm Unit (GANODROP) Malaysian Palm Oil Board, MPOB Many thanks to : Department of Geography, University of Malaya 45

46 Thank You Muchas Gracias