Fast Classification of Large Germinated Fields Via High-Resolution UAV Imagery

Crop breeding consists of the process of editing crop genetic profile for increasing many crop qualities. In order to achieve optimal results, crop breeders have to plant thousands of plants and keep a track of their growth almost daily. This process requires increased man-hour inspection over large fields, which results in poor accuracy due to human fatigue and a time-inefficient strategy. In this letter, two machine vision approaches were compared for classifying three crop germination classes (good, average, and bad). A <italic>naive</italic> approach using a classical segmentation and an unsupervised learning approach using k-means segmentation were compared within a high-resolution unmanned aerial vehicles imagery dataset. Experimental results demonstrate the classification of germinated patches up to 0.05 <inline-formula><tex-math notation="LaTeX">$\text{m}^{2}/\text{patch}$</tex-math></inline-formula> of resolution with a minimum F1-score of <inline-formula><tex-math notation="LaTeX">$\text{76}\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\text{80}\%$</tex-math></inline-formula>, and AUC of <inline-formula><tex-math notation="LaTeX">$\text{95}\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\text{91}\%$</tex-math></inline-formula> for high and low spatial image resolutions, respectively.

[1]  Lei Tian,et al.  Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform , 2016 .

[2]  D. Goodin,et al.  Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries , 2016, Plant Methods.

[3]  Chunhua Zhang,et al.  The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.

[4]  Pietro Perona,et al.  Multiclass recognition and part localization with humans in the loop , 2011, 2011 International Conference on Computer Vision.

[5]  Jorge Torres-Sánchez,et al.  An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery , 2018, Remote. Sens..

[6]  S. Sankaran,et al.  Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review , 2015 .

[7]  Cyrill Stachniss,et al.  UAV-based crop and weed classification for smart farming , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[8]  Graeme L. Hammer,et al.  Multi-Spectral Imaging from an Unmanned Aerial Vehicle Enables the Assessment of Seasonal Leaf Area Dynamics of Sorghum Breeding Lines , 2017, Front. Plant Sci..

[9]  G. Meyer,et al.  Verification of color vegetation indices for automated crop imaging applications , 2008 .

[10]  S. Vermeulen,et al.  Breeding Technologies to Increase Crop Production in a Changing World , 2010 .

[11]  Edward Jones,et al.  A survey of image processing techniques for plant extraction and segmentation in the field , 2016, Comput. Electron. Agric..

[12]  S. Robinson,et al.  Food Security: The Challenge of Feeding 9 Billion People , 2010, Science.

[13]  Hui Peng,et al.  An improved K-means clustering algorithm in agricultural image segmentation , 2013, Other Conferences.

[14]  Jon Louis Bentley,et al.  Quad trees a data structure for retrieval on composite keys , 1974, Acta Informatica.

[15]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[16]  James A. Brass,et al.  Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support , 2004 .

[17]  Roland Siegwart,et al.  weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming , 2017, IEEE Robotics and Automation Letters.

[18]  David J. Hand,et al.  A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems , 2001, Machine Learning.

[19]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[20]  Jorge Torres-Sánchez,et al.  An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops , 2015, Comput. Electron. Agric..