Verification of color vegetation indices for automated crop imaging applications
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Feature extraction of brain CT image based on target shape
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
2-D shape representation using improved Fourier descriptors
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Review: Sensing technologies for precision specialty crop production
Computers and Electronics in Agriculture
Grassland species characterization for plant family discrimination by image processing
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Computers and Electronics in Agriculture
Visual-based plant species identification from crowdsourced data
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Computers and Electronics in Agriculture
Review: Plant species identification using digital morphometrics: A review
Expert Systems with Applications: An International Journal
International Journal of Computational Vision and Robotics
Advanced shape context for plant species identification using leaf image retrieval
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Machine vision based automatic separation of touching convex shaped objects
Computers in Industry
Two-Dimensional locality discriminant projection for plant leaf classification
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Computers and Electronics in Agriculture
A shape-based approach for leaf classification using multiscaletriangular representation
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Multiple view image analysis of freefalling U.S. wheat grains for damage assessment
Computers and Electronics in Agriculture
Automatic classification of legumes using leaf vein image features
Pattern Recognition
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Elliptic Fourier (EF) and discriminant analyses were used to identify young soybean (Glycine max (L.) merrill), sunflower (Helianthus pumilus), redroot pigweed (Amaranthus retroflexus) and velvetleaf (Abutilon theophrasti Medicus) plants, based on leaf shape. Chain encoded, Elliptic Fourier harmonic functions were generated based on leaf boundary. A complexity index of the leaf shape was computed using the variation between consecutive EF functions. Principle component analysis was used to select the Fourier coefficients with the best discriminatory power. Canonical discriminant analysis was used to develop species identification models based on leaf shapes extracted from plant color images during the second and third weeks after germination. The classification results showed that plant species during the third week were successfully identified with an average of correct classification rate of 89.4%. The discriminant model correctly classified on average: 77.9% of redroot pigweed, 93.8% of sunflower, 89.4% of velvetleaf and 96.5% of soybean. Using all of the leaves extracted from the second and the third weeks, the overall classification accuracy was 89.2%. The discriminant model correctly classified 76.4% of redroot pigweed, 93.6% of sunflower, 81.6% of velvetleaf, 91.5% of soybean leaf extracted from trifoliolate and 90.9% of soybean unifoliolate leaves. The Elliptic Fourier shape feature analysis could be an important and accurate tool for weed species identification and mapping.