Paper Info
Title
Rating Iron Deficiency In Soybean Using Image Processing And Decision-Tree Based Models
Abstract
The most efficient way of soybean (Glycine max (L.) Merrill) iron deficiency chlorosis (IDC) management is to select a tolerant cultivar suitable for the specific growing condition. These cultivars are selected by field experts based on IDC visual ratings. However, this visual rating method is laborious, expensive, time-consuming, subjective, and impractical on larger scales. Therefore, a modern digital image-based method using tree-based machine learning classifier models for rating soybean IDC at plot-scale was developed. Data were collected from soybean IDC cultivar trial plots. Images were processed with MATLAB and corrected for light intensity by using a standard color board in the image. The three machine learning models used in this study were decision tree (DT), random forest (RF), and adaptive boosting (AdaBoost). Calculated indices from images, such as dark green color index (DGCI), canopy size, and pixel counts into DGCI ranges and IDC visual scoring were used as input and target variables to train these models. Metrics such as precision, recall, and f1-score were used to assess the performance of the classifier models. Among all three models, AdaBoost had the best performance (average f1-score = 0.75) followed by RF and DT the least. Therefore, a ready-to-use methodology of image processing with AdaBoost model for soybean IDC rating was recommended. The developed method can be easily adapted to smartphone applications or scaled-up using images from aerial platforms.
Year
DOI
Venue
2020
10.3390/rs12244143
REMOTE SENSING
Keywords
DocType
Volume
soybean, iron deficiency chlorosis, image processing, machine learning, AdaBoost, classification
Journal
12
Issue
Citations 
PageRank 
24
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Oveis Hassanijalilian100.34
C. Igathinathane200.34
Sreekala G. Bajwa300.34
John Nowatzki400.34