Early-warning systems for plant diseases are valuable when the systemsprovide timely forecasts that farmers can use to inform their pest manage-ment decisions.Pests and diseases remain a major threat in realizing the potential yield of many improved cultivars in ICRISAT mandate crops. Plant protection need new and innovative early detection, identification and assessment systems.

1. Seeing the Unseen:
Development of Early Detection
Systems for Crop Pests and Diseases
Program Committee meeting of ICRISAT Governing Board, 10th April, 2019
Drs Mamta Sharma, Rajan Sharma, Gopalakrishnan, Jaba
Jagdish, Raju Ghosh & Hari Kishan Sudini
Integrated Crop Management (ICM), Research Program-Asia

2. Problem statement
• Pests and diseases remain a major threat in
realizing the potential yield of many improved
cultivars in ICRISAT mandate crops
• Disease and pest management strategies adopted
based on visible symptoms are often ineffective
and expensive (For eg. Soilborne diseases)
• Visual assessment by human raters is time-
consuming, and error prone
Solution
• Plant protection need new and innovative early
detection, identification and assessment systems
• Hyperspectral sensors and imaging techniques
have shown an enormous potential to provide new
insights into plant-pathogen interactions and for
the early detection of pests and diseases
An example of spectral reflectance curve of
healthy and increasingly damaged plants
(Ian MacRae, University of Minnesota)

3. Hyperspectral imaging system
• RGB, Multispectral & Hyperspectral imaging
• Thermal imaging, Forward Looking Infrared Imagery
(FLIR)
• Microelectromechanical system (MEMS) technology
• Electronic noses (conductive polymer based gas
detectors for sensing VOCs)
• Rapid and real-time
• Non-invasive
• Sensitive
• Integration with remote sensing
• Cost-effective
Attributes Technologies
Hyperspectral sensor setups typically consist of
 hyperspectral sensor
 light source (artificial or sunlight)
 control unit for measuring
Components can be mounted on different carrier
platforms such as vehicles, robots, drones, UAVs

4. Approach - Baseline Data
⁻ Greenhouse setup including susceptible/resistant cultivars
⁻ Artificial infestation with pathogen
⁻ Imaging and reflectance measurements
⁻ Calibration using algorithms
⁻ Field testing and refining
⁻ Greenhouse setup with and without adult moths
⁻ MEMS device & test on site
⁻ Calibration using algorithms, Field testing & refining
Disease and pathogen detection
Pest Detection
Target pests & diseases
Groundnut: stem rot & leaf spots
Chickpea: wilt, Ascochyta blight & Helicoverpa Mahlein et.al., 2018

5. calculate disease
severity
disease severity is linked
to the geographic
distribution
combined distribution
maps of multiple diseases
canopy assessment with a
hyperspectral imaging
Applications/Relevant areas
• Use hyperspectral sensors to identify wavelengths of
interest, this can determine what wavelengths to look at
with air-borne sensors
• Resistance screening
• Assessment of plant defense reactions
• Combined distribution maps of multiple diseases/pests
• Site-specific fungicide/pesticide application
Teaming up
In-house expertise
University of Minnesota
ABI incubate (Lean Agri.)
Mahlein et.al., 2018

6. Thank you for
your attention