Evaluating Critical Disease Occurrence in Grapevine Leaves using CNN: Use-Case in Eastern Europe

Convolutional Neural Networks are Deep Learning algorithms for image classification tasks in the Computer Vision area. Their efficiency was previously evaluated in medical areas, engineering fields and construction applications. Under this category, VGG16 and Avert-CNN (a modified VGG16 version) alg...

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Vydáno v:2023 17th International Conference on Engineering of Modern Electric Systems (EMES) s. 1 - 4
Hlavní autoři: Oprea, Cristina-Claudia, Dragulinescu, Ana-Maria Claudia, Marcu, Ioana-Manuela, Pirnog, Ionut
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 09.06.2023
Témata:
accuracy
Classification algorithms
CNN
Convolutional neural networks
Crops
Deep learning
grapevine leaves diseases occurrence
Pipelines
Real-time systems
Support vector machines
VGG16 architecture
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Shrnutí:Convolutional Neural Networks are Deep Learning algorithms for image classification tasks in the Computer Vision area. Their efficiency was previously evaluated in medical areas, engineering fields and construction applications. Under this category, VGG16 and Avert-CNN (a modified VGG16 version) algorithms can perform real-time identification for diseases occurrence in agricultural plants with high accuracy and fast estimation time. Thus, this research addresses the identification of health status of grapevine leaves using specialized classification algorithms on images taken from PlantVillage dataset and images acquired in a vineyard in the South-East of Romania. The outcome of these classifications consists in predictions based on one of the 5 classes for which the convolutional network was trained, along with a prediction accuracy metric. The classes considered in this process correspond to the state of a healthy plant and a set of 4 distinct diseases that can affect the vine: Black rot, Esca, Leaf blight and Powdery mildew. Based on the achieved results, a novel convolutional neural network architecture is proposed to ensure reliable estimates on the disease's probability of occurrence. Its efficiency in reaching over 94 % prediction accuracy is demonstrated compared to the classic VGG16 which leads to 90.21 % data accuracy and surpasses Random Forest and Support Vector Machine algorithms that achieve 72.87% and 85.82% accuracy, respectively.
DOI:10.1109/EMES58375.2023.10171678