A Benchmarking of Learning Strategies for Pest Detection and Identification on Tomato Plants for Autonomous Scouting Robots Using Internal Databases
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43210%2F19%3A43915593" target="_blank" >RIV/62156489:43210/19:43915593 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1155/2019/5219471" target="_blank" >https://doi.org/10.1155/2019/5219471</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1155/2019/5219471" target="_blank" >10.1155/2019/5219471</a>
Alternative languages
Result language
angličtina
Original language name
A Benchmarking of Learning Strategies for Pest Detection and Identification on Tomato Plants for Autonomous Scouting Robots Using Internal Databases
Original language description
Greenhouse crop production is growing throughout the world and early pest detection is of particular importance in terms of productivity and reduction of the use of pesticides. Conventional eye observation methods are nonefficient for large crops. Computer vision and recent advances in deep learning can play an important role in increasing the reliability and productivity. This paper presents the development and comparison of two different approaches for vision based automated pest detection and identification, using learning strategies. A solution that combines computer vision and machine learning is compared against a deep learning solution. The main focus of our work is on the selection of the best approach based on pest detection and identification accuracy. The inspection is focused on the most harmful pests on greenhouse tomato and pepper crops, Bemisia tabaci and Trialeurodes vaporariorum. A dataset with a huge number of infected tomato plants images was created to generate and evaluate machine learning and deep learning models. The results showed that the deep learning technique provides a better solution because (a) it achieves the disease detection and classification in one step, (b) gets better accuracy, (c) can distinguish better between Bemisia tabaci and Trialeurodes vaporariorum, and (d) allows balancing between speed and accuracy by choosing different models.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
40106 - Agronomy, plant breeding and plant protection; (Agricultural biotechnology to be 4.4)
Result continuities
Project
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Continuities
O - Projekt operacniho programu
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Sensors
ISSN
1687-725X
e-ISSN
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Volume of the periodical
Neuveden
Issue of the periodical within the volume
5 May
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
5219471
UT code for WoS article
000468512700001
EID of the result in the Scopus database
2-s2.0-85066093058