2017
Using engineering tools to identify and quantify biotic and abiotic stress in soybean for customizable agriculture production
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Contributor/Checkoff:
Iowa Soybean Association
Category:
Sustainable Production
Keywords:
Abiotic stressAgricultureLand Use Water supply
Parent Project:
Using engineering tools to identify and quantify biotic and abiotic stress in soybean for customizable agriculture production
Lead Principal Investigator:
Arti Singh, Iowa State University
Co-Principal Investigators:
Baskar Ganapathysubramanian, Iowa State University
Daren Mueller, Iowa State University
Soumik Sarkar, Iowa State University
Asheesh Singh, Iowa State University
Gregory Tylka, Iowa State University
+4 More
Project Code:
450-47-01
Contributing Organization (Checkoff):
Iowa Soybean Association
$53,668
Institution Funded:
Iowa State University
$53,668
Brief Project Summary:

Crop yields are inherently limited by plant stresses (biotic and abiotic). Plant breeders have protected yield from plant stress losses by incorporating resistance genes and developing more resilient cultivars. State-of-the-art High Throughput Phenotyping has unlocked new prospects for field-based phenotyping. What is currently lacking is methodology to quickly screen HTP images into easy-to-use tools that help identify, detect, classify and predict plant diseases. This research aims to use hyperspectral camera and spectro-radiometer to develop disease signatures to distinguish among SCN, SDS, BSR, charcoal rot and IDC; develop algorithms to differentiate diseases with confounding symptoms; develop predictions for disease onset using "disease signatures” and develop an algorithm to count SCN eggs under the microscope rapidly and accurately manner.

Key Benefactors:
farmers, agronomists, Extension agents, soybean breeders, seed companies

Information And Results
Project Deliverables

1. Farmers can scout field for diseases using their phone, making scouting easier.
2. Farmers can scout field though aerial systems to identify diseases, which will help make decisions on pest management in a timely & effective manner.
3. Farmers will save input costs due to their ability to do site-specific management disease management rather than spraying entire fields.
4. Improved profitability due to reduced chemical costs.
5. Provide entrepreneurial opportunities for chemical companies, technology companies, equipment companies, prescription farming companies.
6. Better health and safe environment.

Final Project Results

Update:
The goals of this research is to identify and differentiate biotic stresses under soybean production using hyperspectral camera and remote sensing spectro-radiometer to pave the way for farmers and researchers to use sensors, smartphones and aerial imagery for crop management.

One of our long term goals is to make smartphone apps for farmers to assist in scouting, enabling the farmer to use their smartphone to determine the presence, severity of specific diseases in order to make strategic decisions on disease control.

Research Progress
• Fall of 2017, Review of literature for SCN GWAS in soybean.
• Invited talk at ASA, CSSA and SSSA, on “Towards high throughput stress phenotyping in soybean using machine learning”.Tampa, Florida, USA.
• Fall of 2016, images of charcoal rot resistant and susceptible plants were taken and hyperspectral and spectoradiometer data was collected. The results of charcoal rot hyperspectral signatures were presented in 4th International Plant Phenotyping Network Symposium El Batan, Mexico
• In 2016, more than 25,000 leaflet images were collected from Iowa (USA) fields for five biotic stresses (bacterial leaf blight, bacterial pustule, frogeye leaf spot, Septoria brown spot and SDS) and three abiotic stresses (IDC, potassium deficiency and herbicide injury) as well as healthy leaflets on soybean using a standard imaging protocol. The Deep Convolution Neural Network (DCNN) was designed to automatically differentiate images of eight different stresses. The results of this work were presented in 4th International Plant Phenotyping Network Symposium El Batan, Mexico
• In 2016, a smartphone app (for automated phenotyping) was demonstrated to IA farmers during the ISA board members tour to Iowa State University.
• Spring 2017, hyperspectral disease signatures of root rot disease under progress.

See attached document highlighting Papers, Posters and Talks

View uploaded report Word file

The United Soybean Research Retention policy will display final reports with the project once completed but working files will be purged after three years. And financial information after seven years. All pertinent information is in the final report or if you want more information, please contact the project lead at your state soybean organization or principal investigator listed on the project.