2016
Digital Imaging Technique to Detect and Rate Iron Deficiency Chlorosis (IDC) in Soybeans
Contributor/Checkoff:
Category:
Sustainable Production
Keywords:
Field management Nutrient managementSoil healthTillageYield trials
Parent Project:
This is the first year of this project.
Lead Principal Investigator:
John Nowatzki, North Dakota State University
Co-Principal Investigators:
Sreekala Bajwa, North Dakota State University
Hans Kandel, North Dakota State University
Saravanan Sivarajan, North Dakota State University
+2 More
Project Code:
Contributing Organization (Checkoff):
Institution Funded:
Brief Project Summary:

IDC is an important issue and has a great impact on soybean yield and production losses. The most effective step to manage this problem is to select an IDC tolerant variety suitable to a specific area as IDC occurrence varies widely under different environment conditions. Some scientists suggest that visual ratings at an early stage could help farmers identify its severity and apply iron chelates to prevent yield loss (J. Goos and Hans Kandel, NDSU, personal communication, 2013). But visual rating is too subjective, and requires experts. The method proposed in this study will make use of digital imaging technique to rating iron deficiency chlorosis (IDC) in soybeans using a smartphone camera...

Unique Keywords:
#crop management systems
Information And Results
Project Deliverables

Upon completion of this research, we expect to have gathered the basic information to develop a new accurate and rapid method for rating IDC without the aid of an expert.
1. Smart Phone App: Our long-term research goal is to use the information from this research to develop a smart phone app. With this new method, a farmer or anyone else could rate IDC just using their cellphone in a fast and efficient manner. The variety trials to rate the IDC resistance of new soybean varieties will benefit greatly from this new sensing technology.
2. Enhanced evaluation ability of iron chelate treatments. Trials evaluating the effectiveness of iron chelate treatment on different soybean varieties could greatly benefit from this technology.
3. Additional indices. This study will also evaluate multiple vegetation indices such as DGCI to identify which one is more indicative of IDC.
4. Publications and educational materials. Results of this work will be presented at conferences, grower meetings, field day tours and NDSU websites. The work will also be submitted to peer-reviewed journals for publication.

Final Project Results

Update:
Final Progress Report is downloaded in the File (optional) below

View uploaded report Word file

Digital Imaging Technique to Detect and Rate Iron Deficiency chlorosis (IDC) in Soybeans
Oveis Hassanijalilian, Hans Kandel, Sreekala G. Bajwa, John Nowatzki, Saravanan Sivarajan, Ted Helms
Based on USDA statistics, 5.75 million acres of soybeans were planted in the state of North Dakota in 2015. Soybean yield in the state of North Dakota was almost 185.9 million bushels. One of the factors that reduces soybean yield is lack of useable iron which causes Iron Deficiency Chlorosis (IDC). In 2013 at The North Central U.S., total land area where soybean was grown was approximately 4.7 million acres, where IDC caused 375,000 ton loss in soybean grain production at a value of $120 million per year.
The most effective step to manage this problem is to select an IDC tolerant variety suitable to a specific area as IDC occurrence varies widely under different environment conditions. Tolerance of different varieties to IDC can be determined through IDC visual rating by experts in variety research trials. Some scientists suggest that visual ratings at an early stage could help farmers identify its severity and apply iron chelates to prevent yield loss. But visual rating is too subjective, and requires experts. Leaf color can be used to identify chlorosis in soybean leaves, so digital imaging technique can detect its presence when the crop is growing. Digital imaging technique is used in this study for rating IDC in soybeans with a camera which does not require expertise.
The digital image captured has different bands of Red, Green and Blue (RGB). Research studies at the University of Arkansas showed that the amount of red and blue color scheme indicates how green an image or a plant looks. Therefore, they suggested a vegetation index called Dark Green Color Index (DGCI) based on the color scheme or values of Hue, Saturation and Intensity (HSI). It has been used on corn to detect the nitrogen deficiency.
The greener soybean plots are, the higher the value of DGCI will get. The average value of DGCI for the whole plot was used as a base value to rate IDC. Then by segmenting the image and detecting the low DGCI values for chlorosis, the rating was increased. IDC scores are from 1 to 5 with 0.5 point increments which 1 is no chlorosis, and 5 is severe chlorosis and dead tissues.
Chlorophyll meter values for individual leaflets were also measured. Digital images also were captured from the same leaflets with two different smartphones to determine the ability of the technique to estimate the chlorophyll amount. The DGCI values determined by digital image processing technique was significantly correlated (r2 = 0.89) to Chlorophyll meter reading of the same leaflets. Moreover, the correlation between DGCI values of two different smartphones was significantly high (r2 = 0.98) which means DGCI can be accountable for differences of different cameras.
In summary, these days a lot of people carrying smartphones. They can be used to rate IDC in soybean using DGCI. We are planning to use DGCI accompanied by Unmanned Aerial Vehicle (UAV) to delineate IDC and to detect the growing pattern of IDC in the field. As a result, susceptible regions of the fields can be detected for future management.

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.