01466nam a2200193 a 450000100080000000500110000800800410001902000220006002200140008202400380009610000180013424500910015226001020024330000140034552008490035965300220120865300220123070000200125221438892024-01-23       2022    bl uuuu     u00u1 u    #d  a978-1-6654-3418-8  a2325-65167 a10.1109/ICSC52841.2022.000542DOI1 aMORENO, B. M.  aComputer vision system for identifying on farming weed species.h[electronic resource]  aIn: IEEE International Conference on Semantic Computing (ICSC), 16th, Laguna Hills, CA, USAc2022  a287 - 292  aIn the world, agriculture has been developed by combining new technologies for aid production and profitability while keeping environmental and social responsibility. The sector is primarily responsible to supply food for people, as well as fibers and energy. To keep such results, farmers have faced the need to seek, increasingly rational use of inputs, as is the use of pesticides, plant regulators, and liquid fertilizers. This paper presents a discussion related to the design and development of a computer vision system for precision spraying in the control of weed species into agricultural crops, based on the identification of invasive plants and their quantities. Concepts such as plant segmentation using field-acquired images, leaf features and descriptors, and classification of species are analyzed and implemented in a prototype.  aEmbedded platform  aPlant recognition1 aCRUVINEL, P. E.