Weeds are a crucial threat to agriculture, and in order to preserve crop productivity, spreading agrochemicals is a common practice with a potential negative impact on the environment. Methods that can support intelligent application are needed. Therefore, identification and mapping is a critical step in performing site-specific weed management. Unmanned aerial vehicle (UAV) data streams are considered the best for weed detection due to the high resolution and flexibility of data acquisition and the spatial explicit dimensions of imagery. However, with the existence of unstructured crop conditions and the high biological variation of weeds, it remains a difficult challenge to generate accurate weed recognition and detection models. Two critical barriers to tackling this challenge are related to (1) a lack of case-specific, large, and comprehensive weed UAV image datasets for the crop of interest, (2) defining the most appropriate computer vision (CV) weed detection models to assess the operationality of detection approaches in real case conditions. Deep Learning (DL) algorithms, appropriately trained to deal with the real case complexity of UAV data in agriculture, can provide valid alternative solutions with respect to standard CV approaches for an accurate weed recognition model. In this framework, this paper first introduces a new weed and crop dataset named Chicory Plant (CP) and then tests state-of-the-art DL algorithms for object detection. A total of 12,113 bounding box annotations were generated to identify weed targets (Mercurialis annua) from more than 3000 RGB images of chicory plantations, collected using a UAV system at various stages of crop and weed growth. Deep weed object detection was conducted by testing the most recent You Only Look Once version 7 (YOLOv7) on both the CP and publicly available datasets (Lincoln beet (LB)), for which a previous version of YOLO was used to map weeds and crops. The YOLOv7 results obtained for the CP dataset were encouraging, outperforming the other YOLO variants by producing value metrics of 56.6%, 62.1%, and 61.3% for the mAP@0.5 scores, recall, and precision, respectively. Furthermore, the YOLOv7 model applied to the LB dataset surpassed the existing published results by increasing the mAP@0.5 scores from 51% to 61%, 67.5% to 74.1%, and 34.6% to 48% for the total mAP, mAP for weeds, and mAP for sugar beets, respectively. This study illustrates the potential of the YOLOv7 model for weed detection but remarks on the fundamental needs of large-scale, annotated weed datasets to develop and evaluate models in real-case field circumstances.

0000−0002−4355−0366] , Ignazio Gallo 1[0000−0002−7076−8328] , Alessandro Calefati 1[0000−0003−3860−4785] , and Dimitri Ognibene 2[0000−0002−9454−680X]Abstract. One-class classifiers are trained only with target class samples. Intuitively, their conservative modeling of the class description may benefit classical classification tasks where classes are difficult to separate due to overlapping and data imbalance. In this work, three methods leveraging on the combination of one-class classifiers based on nonparametric models, N-ary Trees and Minimum Spanning Trees class descriptors (MST CD) are proposed. These methods deal with inconsistencies arising from combining multiple classifiers and with spurious connections that MST-CD creates in multimodal class distributions. Experiments on several datasets show that the proposed approach obtains comparable and, in some cases, state-of-theart results.

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In space science and satellite imagery, better resolution of the data information obtained makes images clearer and interpretation more accurate. However, the huge data volume gained by the complex on-board satellite instruments becomes a problem that needs to be managed carefully. To reduce the data volume to be stored and transmitted on-ground, the signals received should be compressed, allowing a good original source representation in the reconstruction step. Image compression covers a key role in space science and satellite imagery and, recently, deep learning models have achieved remarkable results in computer vision. In this paper, we propose a spectral signals compressor network based on deep convolutional autoencoder (SSCNet) and we conduct experiments over multi/hyperspectral and RGB datasets reporting improvements over all baselines used as benchmarks and than the JPEG family algorithm. Experimental results demonstrate the effectiveness in the compression ratio and spectral signal reconstruction and the robustness with a data type greater than 8 bits, clearly exhibiting better results using the PSNR, SSIM, and MS-SSIM evaluation criteria.