Methods for identifying weeds in corn crops using artificial intelligence and image processing

literature review

Authors

DOI:

https://doi.org/10.14488/1676-1901.v24i2.5249

Keywords:

Convolutional Neural Network, Intelligent Agriculture, Image Processing, Smart Weed Control, Corn Crops

Abstract

Research based on Artificial Intelligence and Image Processing with a focus on pest control and management solutions in agriculture has been progressively evolving over the years. Identifying and controlling crop infestations is essential for the effective increase in productivity, which reflects on society's supply chain and food safety management. Thus, the objective, in this phase of the research, was to elaborate a systematic review of the literature, based on search terms to identify methods and techniques that have been used in the identification of weeds in corn crops, with the use of convolutional neural networks. The analysis of the research related to the theme was carried out quantitatively and qualitatively. Nine studies were selected for an in-depth study, seeking to identify factors impacting the results. Also, a comparison of the selected studies was elaborated with analyses and graphs of correlations between keywords and authors. As a result, the studies found demonstrated good performance in their objectives. The concern regarding the quality of the database used, as well as the calibration of the convolutional neural network model, according to the specificity of each study, were highlighted. There has been a significant advance in the application of these models for real-time image processing, allowing an agile and accurate response in the control of agricultural pests.

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Author Biographies

Rodrigo Nunes Wessner, Universidade de Santa Cruz do Sul (UNISC), Santa Cruz do Sul, RS, Brasil.

Mestrando em Sistemas e Processos Industriais e graduado em Ciência da Computação pela Universidade de Santa Cruz do Sul. Desenvolvedor de software com atuação em sistemas bancários e agricultor. Pesquisa temas relacionados à Inteligência Artificial (Redes Neurais Artificiais, Aprendizado de Máquina).

Rejane Frozza, Universidade de Santa Cruz do Sul (UNISC), Santa Cruz do Sul, RS, Brasil.

Doutora em Computação pela Universidade Federal do Rio Grande do Sul (UFRGS-RS), com estágio doutoral sanduíche na Université Joseph Fourier (Grenoble-France). Professora adjunta da Universidade de Santa Cruz do Sul (UNISC-RS), no Departamento de Engenharias, Arquitetura e Computação, no Programa de Pós-Graduação em Sistemas e Processos Industriais - Mestrado e no Programa de Pós-Graduação em Letras - Mestrado e Doutorado. Pesquisa temas relacionados à Inteligência Artificial (Agentes Conversacionais, Agentes Pedagógicos em Sistemas Virtuais de Aprendizagem, Gestão do Conhecimento, Sistemas Multiagentes, Redes Neurais Artificiais, Sistemas Difusos, Sistemas de Raciocínio Baseado em Casos, Aprendizado de Máquina).

Rolf Fredi Molz, Universidade de Santa Cruz do Sul (UNISC), Santa Cruz do Sul, RS, Brasil.

Doutor em Computação pela Universidade Federal do Rio Grande do Sul (UFRGS-RS). Professor titular da Universidade de Santa Cruz do Sul (UNISC-RS) e Pró-Reitor Acadêmico da mesma IES. Avaliador de cursos e instituições de Ensino Superior junto ao Instituto Nacional de Estudos e Pesquisas Educacionais (INEP) - Ministério da Educação; avaliador para acreditação de cursos de Engenharia para o Mercosul, sócio e engenheiro responsável na empresa Imply Tecnologia Eletrônica Ltda. Pesquisa temas relacionados à Arquitetura de Sistemas de Computação (Processamento de Imagens, Redes Neurais).

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Published

2024-06-17

How to Cite

Wessner, R. N., Frozza, R., & Molz, R. F. (2024). Methods for identifying weeds in corn crops using artificial intelligence and image processing: literature review. Revista Produção Online, 24(2), 5249 . https://doi.org/10.14488/1676-1901.v24i2.5249

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Vol. 24 No. 2 (2024): fluxo contínuo

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