Bacterial-fungicidal vine disease detection with proximal aerial images

Székely, Delia Elena; Dobra, Darius; Dobre, Alexandra Elena; Domşa, Victor; Drăghici, Bogdan Gabriel; Ileni, Tudor-Alexandru; Konievic, Robert; Molnár, Szilárd; Sucala, Paul; Zah, Elena; Darabant, Adrian Sergiu; Sándor, Attila; Tamás, Levente

doi:10.1016/j.heliyon.2024.e34017

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e34017

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Bacterial-fungicidal vine disease detection with proximal aerial images

Delia Elena Székely,

Darius Dobra,

Alexandra Elena Dobre

et al.

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2024

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Cited by 2 publications

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Trends in Machine and Deep Learning Techniques for Plant Disease Identification: A Systematic Review

Rodríguez-Lira,

Córdova-Esparza,

Álvarez-Alvarado

et al. 2024

Agriculture

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This review explores the use of machine learning (ML) techniques for detecting pests and diseases in crops, which is a significant challenge in agriculture, leading to substantial yield losses worldwide. This study focuses on the integration of ML models, particularly Convolutional Neural Networks (CNNs), which have shown promise in accurately identifying and classifying plant diseases from images. By analyzing studies published from 2019 to 2024, this work summarizes the common methodologies involving stages of data acquisition, preprocessing, segmentation, feature extraction, and prediction to develop robust ML models. The findings indicate that the incorporation of advanced image processing and ML algorithms significantly enhances disease detection capabilities, leading to the early and precise diagnosis of crop ailments. This can not only improve crop yield and quality but also reduce the dependency on chemical pesticides, contributing to more sustainable agricultural practices. Future research should focus on enhancing the robustness of these models to varying environmental conditions and expanding the datasets to include a wider variety of crops and diseases. CNN-based models, particularly specialized architectures like ResNet, are the most widely used in the studies reviewed, making up 42.36% of all models, with ResNet alone contributing 7.65%. This highlights ResNet’s appeal for tasks that demand deep architectures and sophisticated feature extraction. Additionally, SVM models account for 9.41% of the models examined. The prominence of both ResNet and MobileNet reflects a trend toward architectures with residual connections for deeper networks, alongside efficiency-focused designs like MobileNet, which are well-suited for mobile and edge applications.

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Trends in Machine and Deep Learning Techniques for Plant Disease Identification: A Systematic Review

Rodríguez-Lira,

Córdova-Esparza,

Álvarez-Alvarado

et al. 2024

Agriculture

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A Systematic Review on the Advancements in Remote Sensing and Proximity Tools for Grapevine Disease Detection

Portela,

Sousa,

Araújo-Paredes

et al. 2024

Sensors

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Grapevines (Vitis vinifera L.) are one of the most economically relevant crops worldwide, yet they are highly vulnerable to various diseases, causing substantial economic losses for winegrowers. This systematic review evaluates the application of remote sensing and proximal tools for vineyard disease detection, addressing current capabilities, gaps, and future directions in sensor-based field monitoring of grapevine diseases. The review covers 104 studies published between 2008 and October 2024, identified through searches in Scopus and Web of Science, conducted on 25 January 2024, and updated on 10 October 2024. The included studies focused exclusively on the sensor-based detection of grapevine diseases, while excluded studies were not related to grapevine diseases, did not use remote or proximal sensing, or were not conducted in field conditions. The most studied diseases include downy mildew, powdery mildew, Flavescence dorée, esca complex, rots, and viral diseases. The main sensors identified for disease detection are RGB, multispectral, hyperspectral sensors, and field spectroscopy. A trend identified in recent published research is the integration of artificial intelligence techniques, such as machine learning and deep learning, to improve disease detection accuracy. The results demonstrate progress in sensor-based disease monitoring, with most studies concentrating on specific diseases, sensor platforms, or methodological improvements. Future research should focus on standardizing methodologies, integrating multi-sensor data, and validating approaches across diverse vineyard contexts to improve commercial applicability and sustainability, addressing both economic and environmental challenges.

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Bacterial-fungicidal vine disease detection with proximal aerial images

Cited by 2 publications

References 46 publications

Trends in Machine and Deep Learning Techniques for Plant Disease Identification: A Systematic Review

Trends in Machine and Deep Learning Techniques for Plant Disease Identification: A Systematic Review

A Systematic Review on the Advancements in Remote Sensing and Proximity Tools for Grapevine Disease Detection

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