A study on CNN-based detection of psyllids in sticky traps using multiple image data sources.
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Abstract: Deep learning architectures like Convolutional Neural Networks (CNNs) are quickly becoming the standard for detecting and counting objects in digital images. However, most of the experiments found in the literature train and test the neural networks using data from a single image source, making it difficult to infer how the trained models would perform under a more diverse context. The objective of this study was to assess the robustness of models trained using data from a varying number of sources. Nine different devices were used to acquire images of yellow sticky traps containing psyllids and a wide variety of other objects, with each model being trained and tested using different data combinations. The results from the experiments were used to draw several conclusions about how the training process should be conducted and how the robustness of the trained models is influenced by data quantity and variety.
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Aprendizado profundo, Robustez de modelo, Variedade de dados, Redes neurais, Redes Neurais Convolucionais, Citrus huanglongbing, HLB, Imagens digitais, Deep learning, Model robustness, Data variety, Convolutional Neural Networks, Citrus, Neural networks, Digital images