Abstract
The control of weeds at earlier stages is one of the most relevant tasks in agriculture. However, the detection of plants in environments with uncontrolled conditions is still a challenge. Hence, a deep learning-based approach to address this problem has been proposed in this work. On the one hand, a CNN model based on the UNet architecture has been used to segment plants. On the other hand, a MobileNetV2-based architecture has been implemented to classify different types of plants, in this case, corn, monocotyledon weed species, and dicotyledon weed species. For training the models, a large image dataset was first created and manually annotated. The performance of the segmentation network achieves a Dice Similarity Coefficient (DSC) of 84.27% and a mean Intersection over Union (mIoU) of 74.21%. The performance of the classification model obtained an accuracy, precision, recall, and \(F_1\)-score of 96.36%, 96.68%, 96.34%, and 96.34%, respectively. Then, as the results indicated, the proposed approach is an advantageous alternative for farmers since it provides a way for crop/weed detection in natural field conditions.
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Garibaldi-Márquez, F., Flores, G., Valentín-Coronado, L.M. (2023). Segmentation and Classification Networks for Corn/Weed Detection Under Excessive Field Variabilities. In: Rodríguez-González, A.Y., Pérez-Espinosa, H., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2023. Lecture Notes in Computer Science, vol 13902. Springer, Cham. https://doi.org/10.1007/978-3-031-33783-3_12
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