02750naa a2200241 a 450000100080000000500110000800800410001902400380006010000210009824501380011926000090025752019880026665000200225465300240227465300210229865300220231965300200234165300190236165300250238070000170240570000170242277300690243910013502020-01-17       2006    bl uuuu     u00u1 u    #d7 a10.1016/j.compag.2005.11.0022DOI1 aCAMARGO NETO, J.  aIndividual leaf extractions from young canopy image using Gustafson-Kessel clustering and a genetic algorithm.h[electronic resource]  c2006  aThe 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.  aComputer vision  aAlgoritmo genético  aFuzzy clustering  aGenetic algorithm  aLeaf extraction  aMachine vision  aVisão computacional1 aMEYER, G. E.1 aJONES, D. D.  tComputers and Electronics in Agriculturegv. 51, p. 66-85, 2006.