RT info:eu-repo/semantics/article
T1 A stereoscopic video computer vision system for weed discrimination in rice field under both natural and controlled light conditions by machine learning
A1 Dadashzadeh, Mojtaba
A1 Abbaspour Gilandeh, Yousef
A1 Mesri Gundoshmian, Tarahom
A1 Sabzi, Sajad
A1 Arribas Sánchez, Juan Ignacio
K1 Image processing
K1 Meta-heuristic algorithms neural network (NN)
K1 Optimization
K1 Stereo vision
AB 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.
PB Elsevier
SN 0263-2241
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/73110
UL https://uvadoc.uva.es/handle/10324/73110
LA eng
NO Measurement, September 2024, vol. 237, 115072
NO Producción Científica
DS UVaDOC
RD 05-feb-2025