Potato Disease Classification Using Convolution Neural Networks

Oppenheim, D.; Shani, Guy

doi:10.1017/s2040470017001376

Cited by 115 publications

(47 citation statements)

References 9 publications

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“…Other pre-processing operations have been proposed. Mohanty et al (2016) and Oppenheim and Shani (2017) transformed their images to grayscale. Mohanty et al (2016) compared accuracies obtained on grayscale with those from color images.…”

Section: Deep Learning Applied To Diseases Identificationmentioning

confidence: 99%

“…In some cases, this involved adapting a reference architecture so that it is more efficient in handling the study data. To limit the risk of overfitting due to their small dataset, Oppenheim and Shani (2017) relied on drop-out, a regularization technique based on the random disconnection of links between model layers. In other cases, architectures were more customized.…”

Section: Deep Learning Applied To Diseases Identificationmentioning

confidence: 99%

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Convolutional Neural Networks for the Automatic Identification of Plant Diseases

et al. 2019

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Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Since 2016, many applications for the automatic identification of crop diseases have been developed. These applications could serve as a basis for the development of expertise assistance or automatic screening tools. Such tools could contribute to more sustainable agricultural practices and greater food production security. To assess the potential of these networks for such applications, we survey 19 studies that relied on CNNs to automatically identify crop diseases. We describe their profiles, their main implementation aspects and their performance. Our survey allows us to identify the major issues and shortcomings of works in this research area. We also provide guidelines to improve the use of CNNs in operational contexts as well as some directions for future research.

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Section: Deep Learning Applied To Diseases Identificationmentioning

confidence: 99%

Section: Deep Learning Applied To Diseases Identificationmentioning

confidence: 99%

Convolutional Neural Networks for the Automatic Identification of Plant Diseases

et al. 2019

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“…Machine learning technologies have recently and largely transformed several aspects of agricultural activities [8], [7]. This includes, for example, the development of new approaches for plant diseases identification on isolated plants species such as maize [17], apple [11], wheat [5], or potato [14] ; in addition to yield production [1] and crops quality evaluation [18]. As weed control has a major impact on agricultural production, several studies have been conducted to improve their detection, such as [15].…”

Section: Related Workmentioning

confidence: 99%

Segmentación de instancias para detección automática de malezas y cultivos en campos de cultivo

Mora-Fallas

Goëau

Joly

et al. 2020

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Con base en las recientes aplicaciones exitosas de técnicas de Aprendizaje Profundo en la clasificación, detección y segmentación de plantas, proponemos un enfoque de segmentación de instancias utilizando un modelo Mask R-CNN para la detección de malezas y cultivos en tierras de cultivo. Evaluamos el rendimiento de nuestro modelo con la métrica de precisión promedio de MSCOCO, contrastando el uso de técnicas de aumento de datos. Los resultados obtenidos muestran cómo el modelo se adapta muy bien en este contexto, abriendo nuevas oportunidades para soluciones automatizadas de control de malezas, a gran escala.

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“…Ramcharan et al [11], Amara et al [10], Fuentes et al [17], Habaragamuwa et al [24], Oppenheim [25] are some who have done this.…”

Section: Factors Affecting Classification Accuracymentioning

confidence: 99%

A Review on Deep Learning Based Image Classification of Plant Diseases

Muthukumarana¹,

Aponso²

2020

IJCTE

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Potato Disease Classification Using Convolution Neural Networks

Cited by 115 publications

References 9 publications

Convolutional Neural Networks for the Automatic Identification of Plant Diseases

Convolutional Neural Networks for the Automatic Identification of Plant Diseases

Segmentación de instancias para detección automática de malezas y cultivos en campos de cultivo

A Review on Deep Learning Based Image Classification of Plant Diseases

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