Capsule network-based disease classification for Vitis Vinifera leaves

Andrushia, A. Diana; Neebha, T. Mary; Patricia, A. Trephena; Sagayam, K. Martin; Pramanik, Sabyasachi

doi:10.1007/s00521-023-09058-y

Cited by 20 publications

(2 citation statements)

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“…Capsules make CapsNets utilise space when extracting features and routing by agreement avoids information loss. In addition, Andrushia et al [124] successfully applied CapsNet to the detection of diseases in grapevine leaves and concluded that this model is able to extract more useful features than CNNs due to its ability to map hierarchical pose relationships.…”

Section: Pre-processingmentioning

confidence: 99%

Deep Learning and Machine Learning for Automatic Grapevine Varieties Identification: A Brief Review

Carneiro,

Cunha,

Sousa

2024

Preprint

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The Eurasian grapevine (Vitis vinifera L.) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and inspection activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) and Machine Learning (ML) methods have emerged as a classification alternative to deal with the scarcity of ampelographs and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The classification pipeline of the 31 studies found in the literature was described, highlighting its pros and cons. Most of the studies used DL-based models trained with leaf images acquired in a controlled environment at a maximum distance of 1.2 metres to classify grape varieties. In addition, there is a large gap between practical applications and the datasets used: a great lack of varieties, limited data acquired in the field and a lack of tests on plants under adverse conditions. Potential directions for improving this area of research were also presented.

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Section: Pre-processingmentioning

confidence: 99%

Deep Learning and Machine Learning for Automatic Grapevine Varieties Identification: A Brief Review

Carneiro,

Cunha,

Sousa

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Capsules make CapsNets utilise space when extracting features and routing by agreement avoids information loss. In addition, Andrushia et al [101] successfully applied CapsNet to the detection of diseases in grapevine leaves and concluded that this model is able to extract more useful features than CNNs due to its ability to map hierarchical pose relationships.…”

Section: Architectures and Trainingmentioning

confidence: 99%

Deep Learning for Automatic Grapevine Varieties Identification: A Brief Review

Carneiro,

Cunha,

Sousa

2024

Preprint

View full text Add to dashboard Cite

The Eurasian grapevine (\textit{Vitis vinifera L.}) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role, as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and control activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) methods have emerged as a classification alternative to deal with the scarcity of ampelographers and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The steps of the standard DL-based classification pipeline were described for the 18 most relevant studies found in the literature, highlighting their pros and cons. Potential directions for improving this field of research were also presented.

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