Computer Vision-Based Method for Automatic Detection of Crop Rows in Potato Fields. García-Santillán, I., Peluffo-Ordoñez, D., Caranqui, V., Pusdá, M., Garrido, F., & Granda, P. Advances in Intelligent Systems and Computing, pages 355-366. 2018.
Advances in Intelligent Systems and Computing [link]Website  doi  abstract   bibtex   
This work presents an adaptation and validation of a method for automatic crop row detection from images captured in potato fields (Solanum tuberosum) for initial growth stages based on the micro-ROI concept. The crop row detection is a crucial aspect for autonomous guidance of agricultural vehicles and site-specific treatments application. The images were obtained using a color camera installed in the front of a tractor under perspective projection. There are some issues that can affect the quality of the images and the detection procedure, among them: uncontrolled illumination in outdoor agricultural environments, different plant densities, presence of weeds and gaps in the crop rows. The adapted approach was designed to address these adverse situations and it consists of three linked phases. The main contribution is the ability to detect straight and curved crop rows in potato crops. The performance was quantitatively compared against two existing methods, achieving acceptable results in terms of accuracy and processing time.
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 type = {inbook},
 year = {2018},
 keywords = {Autonomous guidance,Computer vision,Crop row detection,Image segmentation},
 pages = {355-366},
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 abstract = {This work presents an adaptation and validation of a method for automatic crop row detection from images captured in potato fields (Solanum tuberosum) for initial growth stages based on the micro-ROI concept. The crop row detection is a crucial aspect for autonomous guidance of agricultural vehicles and site-specific treatments application. The images were obtained using a color camera installed in the front of a tractor under perspective projection. There are some issues that can affect the quality of the images and the detection procedure, among them: uncontrolled illumination in outdoor agricultural environments, different plant densities, presence of weeds and gaps in the crop rows. The adapted approach was designed to address these adverse situations and it consists of three linked phases. The main contribution is the ability to detect straight and curved crop rows in potato crops. The performance was quantitatively compared against two existing methods, achieving acceptable results in terms of accuracy and processing time.},
 bibtype = {inbook},
 author = {García-Santillán, Iván and Peluffo-Ordoñez, Diego and Caranqui, Víctor and Pusdá, Marco and Garrido, Fernando and Granda, Pedro},
 doi = {10.1007/978-3-319-73450-7_34},
 chapter = {Computer Vision-Based Method for Automatic Detection of Crop Rows in Potato Fields},
 title = {Advances in Intelligent Systems and Computing}
}

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