RT info:eu-repo/semantics/article
T1 Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields
A1 Dadashzadeh, Mojtaba
A1 Abbaspour Gilandeh, Yousef
A1 Mesri Gundoshmian, Tarahom
A1 Sabzi, Sajad
A1 Hernández Hernández, José Luis
A1 Hernández Hernández, Mario
A1 Arribas Sánchez, Juan Ignacio
K1 Sustainable agriculture
K1 Agricultura sostenible
K1 Eco-friendly techniques
K1 Técnicas ecológicas
AB Site-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively.
PB MDPI
SN 2223-7747
YR 2020
FD 2020
LK https://uvadoc.uva.es/handle/10324/52318
UL https://uvadoc.uva.es/handle/10324/52318
LA eng
NO Plants, 2020, vol. 9, n. 5, 559
NO Producción Científica
DS UVaDOC
RD 25-nov-2024