Weed detection in multi-spectral images of cotton fields

A means for automatic detection and evaluation of weeds in the field was developed and tested; it was based on an acousto-optic tunable hyperspectral sensor and a detection algorithm. The algorithm that was developed used spectral reflectance properties and robust statistics features for weed detection. Soil-crop segmentation was done with two spectral channels, chosen from 100 channels available from the hyperspectral sensor. Weed detection was based on texture features, extracted from the segmented images. The algorithm was applied to a database of images of cotton plants and weeds, in their early stages of development. The results showed a good detection ability. The occurrence of weeds was detected in all images; the weed-infested area was estimated with 14% error, and the false detection rate was 15%.

[1]  Shiv O. Prasher,et al.  USE OF AIRBORNE MULTI-SPECTRAL IMAGERY FOR WEED DETECTION IN FIELD CROPS , 2002 .

[2]  Murray Eden,et al.  Fundamentals of Digital Optics , 1996 .

[3]  Rama Chellappa,et al.  Classification of textures using Gaussian Markov random fields , 1985, IEEE Trans. Acoust. Speech Signal Process..

[4]  D. K. Giles,et al.  Precision weed control system for cotton , 2002 .

[5]  R. B. Brown,et al.  Remote Sensing for Identification of Weeds in No-till Corn , 1994 .

[6]  Dennis R. Suhre,et al.  Spatial resolution of imaging noncollinear acousto-optic tunable filters , 1992 .

[7]  Thomas G. Dietterich.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[8]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[9]  P. Katzka,et al.  Rapid scanning fluorescence spectroscopy using an acousto‐optic tunable filter , 1987 .

[10]  Performance characteristics of an acousto-optic tunable filter for optical spectrometry , 1992 .

[11]  Dallas E. Peterson,et al.  WEED DETECTION USING COLOR MACHINE VISION , 2000 .

[12]  Marvin R Paulsen,et al.  Optical methods for nondestructive quality evaluation of agricultural and biological materials , 1985 .

[13]  George E. Meyer,et al.  Shape features for identifying young weeds using image analysis , 1994 .

[14]  M. S. Moran,et al.  Remote Sensing for Crop Management , 2003 .

[15]  Lei Tian,et al.  COLOR IMAGE SEGMENTATION WITHGENETIC ALGORITHM FOR IN-FIELD WEED SENSING , 2000 .

[16]  Jin-Young Jeong,et al.  AE—Automation and Emerging Technologies: Weed–plant Discrimination by Machine Vision and Artificial Neural Network , 2002 .

[17]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[18]  Ning Wang,et al.  DESIGN OF AN OPTICAL WEED SENSOR USINGPLANT SPECTRAL CHARACTERISTICS , 2001 .

[19]  M. S. Kim,et al.  MULTISPECTRAL DETECTION OF FECAL CONTAMINATION ON APPLES BASED ON HYPERSPECTRAL IMAGERY: PART I. APPLICATION OF VISIBLE AND NEAR–INFRARED REFLECTANCE IMAGING , 2002 .

[20]  C D Tran,et al.  Acousto-optic tunable filter as a polychromator and its application in multidimensional fluorescence spectrometry. , 1992, Analytical chemistry.

[21]  Martial Hebert,et al.  Spectro-polarimetric imager for intelligent transportation systems , 1998, Other Conferences.

[22]  Josep Peñuelas,et al.  An AOTF-based hyperspectral imaging system for field use in ecophysiological and agricultural applications , 2001 .

[23]  W. R. Windham,et al.  Hyperspectral Imaging for Detecting Fecal and Ingesta Contaminants on Poultry Carcasses , 2002 .