Submitted to: Industrial Crops and Products
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/6/2011
Publication Date: 5/14/2011
Citation: Thorp, K.R., Dierig, D.A. 2011. Color image segmentation approach to monitor flowering in lesquerella. Industrial Crops and Products. 34(1):1150-1159. Interpretive Summary: Lesquerella seed oil may be used as a biorenewable petroleum substitute in the production of many industrial products, including cosmetics, coatings, plastics, and greases. It also has application as a biorenewable diesel fuel additive. Several issues related to crop management and plant breeding must be resolved before the crop can be produced commercially. In this study, we investigated a digital imaging approach that could be used in management and breeding of lesquerella crops. We demonstrated that digital images may be useful for monitoring the progression of flowering in lesquerella. Information about lesquerella flowering will aid breeders in the selection of optimum varieties and will aid growers with irrigation management and harvest decisions. The imaging approach developed in this work provided information more inexpensively, more practically, and with greater accuracy than previously reported approaches. The results of this study advance the science of digital image processing for applications in agricultural crop management. Results will benefit plant breeders, growers, and others aiming to develop lesquerella into a commercially viable oilseed crop for production of biorenewable products.
Technical Abstract: Lesquerella (Lesquerella fendleri) seed soil has been proposed as a petroleum alternative in the production of many industrial products, but several crop management and breeding challenges must be addressed before the crop will be grown commercially. Lesquerella canopies characteristically exhibit quite prominent and vibrant yellow flowers at anthesis, and remote detection of lesquerella flowering patterns can provide useful crop development information to aid management and breeding decisions. In the present study, we used a consumer-grade digital camera to collect images 2 m above lesquerella canopies throughout two growing seasons. Biomass samples within 0.125 m2 areas were also regularly collected and processed to obtain flower numbers. Image processing algorithms were developed to extract information on lesquerella flower features from the images. Key features of the image processing approach included an image transformation to the hue, saturation, and intensity (HSI) color space and a Monte Carlo approach to address uncertainty in HSI parameters used for image segmentation. Flower numbers were estimated from image-based flower cover percentage with root mean squared errors that ranged from 159 to 194 flowers, which was better than the reported results for other studies with a similar objective. Attempts to resolve individual flowers were less successful due to the complexity of the flowering patterns within the image scenes. Digital imaging offers an inexpensive and quite practical means for remote monitoring of flowering patterns in lesquerella canopies.