Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Verification of color vegetation indices for automated crop imaging applications
Computers and Electronics in Agriculture
Mean-shift-based color segmentation of images containing green vegetation
Computers and Electronics in Agriculture
Intelligent Service Robotics
Original paper: Real time feature extraction and Standard Cutting Models fitting in grape leaves
Computers and Electronics in Agriculture
Expert Systems with Applications: An International Journal
Comparative study of segmentation methods for tree leaves extraction
Proceedings of the International Workshop on Video and Image Ground Truth in Computer Vision Applications
An Adaptive Thresholding algorithm of field leaf image
Computers and Electronics in Agriculture
Research experience for teachers: data analysis & mining, visualization, and image processing
Proceedings of the 45th ACM technical symposium on Computer science education
Efficient segmentation of leaves in semi-controlled conditions
Machine Vision and Applications
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The extraction of individual concealed leaves from images of complex plant canopies is a necessary step for taxonomic feature acquisition, species identification, and mapping using a modern personal computer. A new system for individual leaflet extraction was developed and tested, based on connected components, fuzzy clustering and a genetic optimization algorithm. Color images were taken of young, but sparse green canopies, grown in both greenhouse and field conditions. Some images contained individual leaves as connected components, which were readily apparent after separation of the vegetation from its background. Fragments of all other leaves imbedded in the canopy were obtained using the Gustafson-Kessel (GK) clustering algorithm. Each leaf fragment was labeled and placed in a variable length data structure called a chromosome, which represented selected leaf fragments and its neighbors. A genetic algorithm was then used to systematically reassemble the fragments of non-occluded, individual leaves. System performance was evaluated by comparing the number of individual leaves extracted by the computer per plant or plant canopy connected component for various soil/residue backgrounds and time after emergence. 83.5% of the plants in the second week produced at least one computer-extracted leaf for identification. Ninty-two percent of the plants had at least one computer extracted leaf by the third week. 84.7% had more than three computer extracted leaves for identification in the third week. Images of young field plants in multiple species clusters resulted in a 46% leaf extraction rate, but with at least one leaf per connected canopy component. Soybean and velvetleaf leaflets were the easiest to extract. Once individual leaves are extracted, they can be classified using traditional shape and textural feature methods. Computerized individual leaf extraction could assist plant identification and mapping, needed for weed control and crop management.