Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing

Kusumo, Budiarianto Suryo; Heryana, Ana; Mahendra, Oka; Pardede, Hilman F.

doi:10.1109/ic3ina.2018.8629507

Cited by 93 publications

(48 citation statements)

References 9 publications

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“…These groups are then given a membership function defined by the truth value. A novel approach is presented by Kusumo et al [19] this approach had aimed to identify the infection in wheat crop digital images. The data set is provided with both positive and negative images.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Vision Based Plant Disease Classification Through Leaf Imagining

Rehman¹,

Anwar²,

Tufail³

2020

ISI

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Plants disease identification plays a major role in agriculture yield prevention. Traditionally, manual plant surface examination is conducted which is time-consuming and relatively less efficient. Therefore, this study incorporates machine vision-based techniques, for plant disease identification (i.e. healthy leaf, Alternaria Alternate, and bacterial blight). The developed method employs a dataset comprised of more than 10,000 data points. Initially, Image processing is performed followed by image pre-processing techniques for noise removal. Subsequently, image segmentation is performed for the region of interest (ROI). A multiclass classifier is introduced for plant disease analysis, which results in a percentage of disease spreading in healthy leaf. The results were compared with other methods and it is evident that the developed method has shown 95% accuracy in plant disease identification.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Vision Based Plant Disease Classification Through Leaf Imagining

Rehman¹,

Anwar²,

Tufail³

2020

ISI

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“…Maize is one of the dominant food crops, and its planting area and yield are the largest in the world after wheat and rice (Zhang et al, 2018). Apart from being an important food for humans, maize can also be planted for cooking oil, animal fodder, maize flour, and other uses; it is also widely used for industrial raw materials (Kusumo et al, 2018). However, maize is susceptible to various diseases.…”

Section: Introductionmentioning

confidence: 99%

“…This method is subjective, labour‐intensive, inefficient, and expensive for farms, and cannot be carried out on a large scale (Al‐Hiary et al, 2011; Ding & Taylor, 2016). Nevertheless, with the advancement of digital cameras and increasing computational capacity, new ways for maize disease identification are available, and increasing attention has been paid to the research and application of image processing and machine learning (Kusumo et al, 2018; Lu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Attention embedded lightweight network for maize disease recognition

et al. 2020

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Crop disease has a negative impact on food security. If diverse crop diseases are not identified in time, they can spread and influence the quality, quantity, and production of grain. Severe crop diseases can even result in complete failure of the harvest. Recent developments in deep learning, particularly convolutional neural networks (CNNs), have exhibited impressive performance in both image recognition and classification. In this study, we propose a novel network architecture, namely Mobile‐DANet, to identify maize crop diseases. Based on DenseNet, we retained the structure of the transition layers and used the depthwise separable convolution in dense blocks instead of the traditional convolution layers, and then embedded the attention module to learn the importance of interchannel relationship and spatial points for input features. In addition, transfer learning was used in model training. By this means, we improved the accuracy of the model while saving more computational power than deep CNNs. This model achieved an average accuracy of 98.50% on the open maize data set, and even with complicated backdrop conditions, Mobile‐DANet realized an average accuracy of 95.86% for identifying maize crop diseases on a local data set. The experimental findings show the effectiveness and feasibility of the Mobile‐DANet. Our data set is available at https://github.com/xtu502/maize-disease-identification.

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“…De acordo com o autor, o espaço de cor LUV é usado para eliminar o impacto na dominância de cores. Kusumo et al (2018) apresentam uma abordagem para detecção automática de doenças na folha do milho. Neste trabalho são investigadas várias características com o intuito de identificar aquelas que melhor discriminam as características da doença e apresentam maior robustez em relação a variabilidade dos dados.…”

Section: Extração De Característicasunclassified

Método automático para detecção de nematóides em lavoura cafeeira usando imagens aéreas.

Oliveira¹

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Agradeço imensamente a Deus que me ampara a cada passo desta jornada. Minha gratidão ao meu orientador Prof o. Jefferson Rodrigo de Souza pela oportunidade de trabalhar e me aprofundar nesta temática, pelos ensinamentos, incentivo e disponibilidade. Meu sincero agradecimento a minha coorientadora Prof a. Elaine Ribeiro Faria pelos ensinamentos, paciência e incentivo, além dos feedbacks que foram fundamentais para o meu crescimento. Obrigado aos professores e funcionários da PPGCO, em especial aos professores Fabiano Dorça, Gina Maira Barbosa, Márcia Aparecida e André Backes e também ao Erisvaldo e a Sônia pelo grande auxílio nas questões administrativas. Minha profunda gratidão a minha família Ana Carolina, Claudemira e Ricardo pelo carinho, cuidado e amor. Em especial, a minha mãe Claudemira pelo empenho e apoio incondicional em toda minha carreira acadêmica. Obrigado aos meus amigos, em especial ao Antônio Martins pelas valiosas conversas e conselhos, além de toda a amizade. Agradeço à CNPq pelo apoio financeiro durante a realização do trabalho. A todos, o meu muito obrigado! "A última medida de um homem não é onde ele se posiciona em momentos de conforto e conveniência, mas onde ele se posiciona em momentos de desafio e controvérsia."

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Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing

Cited by 93 publications

References 9 publications

Machine Vision Based Plant Disease Classification Through Leaf Imagining

Machine Vision Based Plant Disease Classification Through Leaf Imagining

Attention embedded lightweight network for maize disease recognition

Método automático para detecção de nematóides em lavoura cafeeira usando imagens aéreas.

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