Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/26147
Autoria: | Domingues, T. Brandão, T. Ferreira, J. |
Data: | 2022 |
Título próprio: | Machine learning for detection and prediction of crop diseases and pests: A comprehensive survey |
Volume: | 12 |
Número: | 9 |
Referência bibliográfica: | Domingues, T., Brandão, T., & Ferreira, J. (2022). Machine learning for detection and prediction of crop diseases and pests: A comprehensive survey. Agriculture, 12(9): 1350. http://dx.doi.org/10.3390/agriculture12091350 |
ISSN: | 2077-0472 |
DOI (Digital Object Identifier): | 10.3390/agriculture12091350 |
Palavras-chave: | Plant diseases and pests Classification Detection Forecasting Precision farming Machine learning Smart farming |
Resumo: | Considering the population growth rate of recent years, a doubling of the current worldwide crop productivity is expected to be needed by 2050. Pests and diseases are a major obstacle to achieving this productivity outcome. Therefore, it is very important to develop efficient methods for the automatic detection, identification, and prediction of pests and diseases in agricultural crops. To perform such automation, Machine Learning (ML) techniques can be used to derive knowledge and relationships from the data that is being worked on. This paper presents a literature review on ML techniques used in the agricultural sector, focusing on the tasks of classification, detection, and prediction of diseases and pests, with an emphasis on tomato crops. This survey aims to contribute to the development of smart farming and precision agriculture by promoting the development of techniques that will allow farmers to decrease the use of pesticides and chemicals while preserving and improving their crop quality and production. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
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article_90301.pdf | 570,69 kB | Adobe PDF | Ver/Abrir |
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