PMID- 32349459
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20200928
IS  - 2223-7747 (Print)
IS  - 2223-7747 (Electronic)
IS  - 2223-7747 (Linking)
VI  - 9
IP  - 5
DP  - 2020 Apr 27
TI  - Weed Classification for Site-Specific Weed Management Using an Automated Stereo 
      Computer-Vision Machine-Learning System in Rice Fields.
LID - 10.3390/plants9050559 [doi]
LID - 559
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.
FAU - Dadashzadeh, Mojtaba
AU  - Dadashzadeh M
AD  - Department of Biosystems Engineering, College of Agriculture and Natural 
      Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran.
FAU - Abbaspour-Gilandeh, Yousef
AU  - Abbaspour-Gilandeh Y
AUID- ORCID: 0000-0002-9999-7845
AD  - Department of Biosystems Engineering, College of Agriculture and Natural 
      Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran.
FAU - Mesri-Gundoshmian, Tarahom
AU  - Mesri-Gundoshmian T
AD  - Department of Biosystems Engineering, College of Agriculture and Natural 
      Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran.
FAU - Sabzi, Sajad
AU  - Sabzi S
AD  - Department of Biosystems Engineering, College of Agriculture and Natural 
      Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran.
FAU - Hernandez-Hernandez, Jose Luis
AU  - Hernandez-Hernandez JL
AD  - Division of Research and Graduate Studies, TecNM/Technological Institute of 
      Chilpancingo, Chilpancingo 39070, Mexico.
FAU - Hernandez-Hernandez, Mario
AU  - Hernandez-Hernandez M
AUID- ORCID: 0000-0001-8330-4779
AD  - Faculty of Engineering, Autonomous University of Guerrero, Chilpancingo 39070, 
      Mexico.
FAU - Arribas, Juan Ignacio
AU  - Arribas JI
AUID- ORCID: 0000-0002-7486-6152
AD  - Department of Teoria de la Senal y Comunicaciones, University of Valladolid, 
      47011 Valladolid, Spain.
AD  - Castilla-Leon Neuroscience Institute, University of Salamanca, 37007 Salamanca, 
      Spain.
LA  - eng
PT  - Journal Article
DEP - 20200427
PL  - Switzerland
TA  - Plants (Basel)
JT  - Plants (Basel, Switzerland)
JID - 101596181
PMC - PMC7284472
OTO - NOTNLM
OT  - eco-friendly technique
OT  - metaheuristic algorithm
OT  - rice field
OT  - site-specific management
OT  - sustainable agriculture
OT  - weed
COIS- The authors declare no conflicts of interest. The funders had no role in the 
      design of the study; in the collection, analyses, or interpretation of data; in 
      the writing of the manuscript, or in the decision to publish the results.
EDAT- 2020/05/01 06:00
MHDA- 2020/05/01 06:01
PMCR- 2020/04/27
CRDT- 2020/05/01 06:00
PHST- 2020/03/27 00:00 [received]
PHST- 2020/04/24 00:00 [revised]
PHST- 2020/04/24 00:00 [accepted]
PHST- 2020/05/01 06:00 [entrez]
PHST- 2020/05/01 06:00 [pubmed]
PHST- 2020/05/01 06:01 [medline]
PHST- 2020/04/27 00:00 [pmc-release]
AID - plants9050559 [pii]
AID - plants-09-00559 [pii]
AID - 10.3390/plants9050559 [doi]
PST - epublish
SO  - Plants (Basel). 2020 Apr 27;9(5):559. doi: 10.3390/plants9050559.