Browsing by Author "Delpiano, José"

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    A two-stage deep learning strategy for weed identification in grassfields
    (2024) Calderara Cea, Felipe Antonio; Torres Torriti, Miguel Attilio; Auat Cheein, Fernando; Delpiano, José
    Machine vision strategies for weed identification, whether in industrial crops or grassfields, are fundamental to the development of automated removal systems necessary to increase agricultural yield and field maintenance efficiency. Identifying plant species considered invasive on grassfields is particularly challenging due to reduced color and morphological contrast, as well as phenotypic variability. This work presents a two-stage weed identification strategy using visible spectrum images. The first stage employs a convolutional siamese neural network to identify candidate regions that may contain weeds of irregular or regular morphology. The second stage employs a convolutional neural network to confirm the presence of irregular morphology weeds. The results of each stage are combined to produce an output containing a per-pixel probability of irregular weed and bounding boxes for the morphologically regular weed. The two-stage strategy has an accuracy score of 97.16% and a balanced accuracy score of 89.94% and macro F1 score of 81.14%. In addition to the good performance scores obtained with the proposed approach, it is to be noted that the convolutional Siamese network allows achieving a good performance with a relatively small dataset compared to other strategies that employ data-intensive training phases for optimizing the convolutional neural networks. The results were obtained with a dataset of weeds that has been made publicly available, as well as the neural network models and associated computer code. The dataset contains samples Trifolium repens and Lectuca virosa on grass obtained with two different cameras under varying illumination conditions and different geographic locations. The lightweight nature of the proposed strategy provides a solution amenable to implementation using currently existing embedded computer technology for real-time weed detection.
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    Exploring the Potential of Reconstructed Multispectral Images for Urban Tree Segmentation in Street View Images
    (2024) Arévalo Ramírez, Tito; Alfaro, Analí; Saavedra, José M.; Recabarren, Matías; Ponce-Donoso, Mauricio; Delpiano, José
    Deep learning has gained popularity in recent years for reconstructing hyperspectral and multispectral images, offering cost-effective solutions and promising results. Research on hyperspectral image reconstruction feeds deep learning models with images at specific wavelengths and outputs images in other spectral bands. Although encouraging results of previous works, it should be determined to what extent the reconstructed information can lead to an advantage over the captured images. In this context, the present work inspects whether or not reconstructed spectral images add relevant information to segmentation networks for improving urban tree identification. Specifically, we generate red-edge (ReD) and near-infrared (NIR) images from RGB images using a conditional Generative Adversarial Network (cGAN). The training and validation are carried out with 5770 multispectral images obtained after a custom data augmentation process using an urban hyperspectral dataset. The testing outcomes reveal that ReD and NIR can be generated with an average structural similarity index measure of 0.93 and 0.88, respectively. Next, the cGAN generates ReD and NIR information of two RGB-based urban tree datasets (i.e., Jekyll, 3949 samples, and Arbocensus, 317 samples). Subsequently, DeepLabV3 and SegFormer segmentation networks are trained, validated, and tested using RGB, RGB+ReD, and RGB+NIR images from Jekyll and Arbocensus datasets. The experiments show that reconstructed multispectral images might not add information to segmentation networks that enhance their performance. Specifically, the p-values from a T-test show no significant difference between the performance of segmentation networks.