2026-04-22T22:18:42Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/731102025-01-08T20:01:57Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

Dadashzadeh, Mojtaba Abbaspour Gilandeh, Yousef Mesri Gundoshmian, Tarahom Sabzi, Sajad Arribas Sánchez, Juan Ignacio 2025-01-08T08:33:33Z 2025-01-08T08:33:33Z 2024 Measurement, September 2024, vol. 237, 115072 0263-2241 https://uvadoc.uva.es/handle/10324/73110 10.1016/j.measurement.2024.115072 115072 Measurement 237 A site-specific weed detection and classification system was implemented with a stereoscopic video camera to reduce the adverse effects of chemical herbicides in rice field. A computer vision and meta-heuristic hybrid NN-ICA classifier were used to accurately discriminate between two weed varieties and rice plants, under either natural light (NLC) or controlled light conditions (CLC). Preprocessing, segmentation, and matching procedures were performed on images coming from either right or left camera channels. Most discriminant features were selected from average, either arithmetic or geometric, images using a NN-PSO algorithm. Accuracy classification results with the stereo computer vision system under NLC were 85.71 % for the arithmetic mean (AM) and 85.63 % for the geometric mean (GM), test set. At the same time, accuracy classification results of the computer vision system under CLC reached 96.95 % for the AM case and 94.74 % for the GM case, being consistently higher than those under NLC. eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2024 The Author(s) Attribution-NonCommercial-NoDerivatives 4.0 Internacional A stereoscopic video computer vision system for weed discrimination in rice field under both natural and controlled light conditions by machine learning info:eu-repo/semantics/article