A Deep Learning-Based Approach for the Detection of Infested Soybean Leaves

We address the soybean leaves infestation problem by proposing a robust classification model that can reliably detect infests by Diabrotica speciosa and caterpillars. Our transfer-learning based model uses a VGG19 convolutional neural network to classify the soybean leaves and we achieve balanced ac...

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Bibliographic Details
Published inIEEE access Vol. 11; pp. 99670 - 99679
Main Authors Farah, Niklas, Drack, Nicolas, Dawel, Hannah, Buettner, Ricardo
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Autonomous aerial vehicles
Biological system modeling
caterpillars
Convolutional neural network
Convolutional neural networks
Crops
Deep learning
Diabrotica speciosa
Economics
Infestation
Machine learning
Pesticides
Pests
Plant diseases
plant infestation
Plants (biology)
Production
soybean
Soybeans
VGG-19
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Summary:We address the soybean leaves infestation problem by proposing a robust classification model that can reliably detect infests by Diabrotica speciosa and caterpillars. Our transfer-learning based model uses a VGG19 convolutional neural network to classify the soybean leaves and we achieve balanced accuracies between 93.71% and 94.16% on unseen testing data. This sets a new benchmark and outperforms previous work using the same dataset. Our work has theoretical and practical implications. The soybean plays a crucial role in the agricultural industry. Infestation of soybeans leads to enormous economic and environmental losses. With our model presented here, an early and accurate detection to control the spread of plant pests is possible, which reduces economic and ecological damages.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3313978