Anomaly Detection for Internet of Things Time Series Data Using Generative Adversarial Networks With Attention Mechanism in Smart Agriculture

Cheng, Weijun; Ma, Ting; Wang, Xiaoting; Wang, Gang

doi:10.3389/fpls.2022.890563

Cited by 21 publications

(9 citation statements)

References 37 publications

(59 reference statements)

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“…They can effectively detect sensor failures, which helps make the right decisions about irrigation. For example, the use of Autoencoders [98] and GANs [99] should be further investigated considering the recent promising results in [100,101].…”

Section: New Research Horizonsmentioning

confidence: 99%

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

Del-Coco,

Leo,

Carcagnì

2024

Information

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The management of water resources is becoming increasingly important in several contexts, including agriculture. Recently, innovative agricultural practices, advanced sensors, and Internet of Things (IoT) devices have made it possible to improve the efficiency of water use. However, it is the application of control strategies based on advanced machine learning techniques that enables the adoption of smart irrigation scheduling and the immediate economic, social, and environmental benefits. This challenging research area has attracted the attention of many researchers worldwide, who have proposed several technological and methodological solutions. Unfortunately, the results of these scientific efforts have not yet been categorized in a thematic survey, making it difficult to understand how far we are from optimal water management based on machine learning. This paper fills this gap by focusing on smart irrigation systems with an emphasis on machine learning. More specifically, the generic structure of a smart agriculture system is presented, and existing machine learning strategies and available datasets are discussed. Furthermore, several open issues are identified, especially in the processing of long-term data, also due to the lack of corresponding annotated datasets. Finally, some interesting future research directions to be pursued in order to build scalable, domain-independent approaches are proposed.

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Section: New Research Horizonsmentioning

confidence: 99%

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

Del-Coco,

Leo,

Carcagnì

2024

Information

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“…Recently, attention mechanism has become a research hotspot for network design in NLP and CV and has been widely applied [36][37][38]. The attention mechanism allows the network to dynamically focus on the target region of the image, highlighting useful information and suppressing attention to other information, which enhances the feature extraction ability and improves the target localization performance.…”

Section: Gam Modulementioning

confidence: 99%

Multi-Crop Navigation Line Extraction Based on Improved YOLO-v8 and Threshold-DBSCAN under Complex Agricultural Environments

Shi,

Bai,

Zhou

et al. 2023

Agriculture

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Field crops are usually planted in rows, and accurate identification and extraction of crop row centerline is the key to realize autonomous navigation and safe operation of agricultural machinery. However, the diversity of crop species and morphology, as well as field noise such as weeds and light, often lead to poor crop detection in complex farming environments. In addition, the curvature of crop rows also poses a challenge to the safety of farm machinery during travel. In this study, a combined multi-crop row centerline extraction algorithm is proposed based on improved YOLOv8 (You Only Look Once-v8) model, threshold DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering, least squares method, and B-spline curves. For the detection of multiple crops, a DCGA-YOLOv8 model is developed by introducing deformable convolution and global attention mechanism (GAM) on the original YOLOv8 model. The introduction of deformable convolution can obtain more fine-grained spatial information and adapt to crops of different sizes and shapes, while the combination of GAM can pay more attention to the important feature areas of crops. The experimental results shown that the F1-score and mAP value of the DCGA-YOLOv8 model for Cabbage, Kohlrabi, and Rice are 96.4%, 97.1%, 95.9% and 98.9%, 99.2%, 99.1%, respectively, which has good generalization and robustness. A threshold-DBSCAN algorithm was proposed to implement clustering for each row of crops. The correct clustering rate for Cabbage, Kohlrabi and Rice reaches 98.9%, 97.9%, and 100%, respectively. And LSM and cubic B-spline curve methods were applied to fit straight and curved crop rows, respectively. In addition, this study constructed a risk optimization function for the wheel model to further improve the safety of agricultural machines operating between crop rows. This indicates that the proposed method can effectively realize the accurate recognition and extraction of navigation lines of different crops in complex farmland environment, and improve the safety and stability of visual navigation and field operation of agricultural machines.

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“…The idea of anomaly detection in agriculture was also explored in [59]. The authors performed outlier detection on IoT data using deep learning methods.…”

Section: Anomaly Detectionmentioning

confidence: 99%

AgriSen-COG, a Multicountry, Multitemporal Large-Scale Sentinel-2 Benchmark Dataset for Crop Mapping Using Deep Learning

Selea

2023

Remote Sensing

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With the increasing volume of collected Earth observation (EO) data, artificial intelligence (AI) methods have become state-of-the-art in processing and analyzing them. However, there is still a lack of high-quality, large-scale EO datasets for training robust networks. This paper presents AgriSen-COG, a large-scale benchmark dataset for crop type mapping based on Sentinel-2 data. AgriSen-COG deals with the challenges of remote sensing (RS) datasets. First, it includes data from five different European countries (Austria, Belgium, Spain, Denmark, and the Netherlands), targeting the problem of domain adaptation. Second, it is multitemporal and multiyear (2019–2020), therefore enabling analysis based on the growth of crops in time and yearly variability. Third, AgriSen-COG includes an anomaly detection preprocessing step, which reduces the amount of mislabeled information. AgriSen-COG comprises 6,972,485 parcels, making it the most extensive available dataset for crop type mapping. It includes two types of data: pixel-level data and parcel aggregated information. By carrying this out, we target two computer vision (CV) problems: semantic segmentation and classification. To establish the validity of the proposed dataset, we conducted several experiments using state-of-the-art deep-learning models for temporal semantic segmentation with pixel-level data (U-Net and ConvStar networks) and time-series classification with parcel aggregated information (LSTM, Transformer, TempCNN networks). The most popular models (U-Net and LSTM) achieve the best performance in the Belgium region, with a weighted F1 score of 0.956 (U-Net) and 0.918 (LSTM).The proposed data are distributed as a cloud-optimized GeoTIFF (COG), together with a SpatioTemporal Asset Catalog (STAC), which makes AgriSen-COG a findable, accessible, interoperable, and reusable (FAIR) dataset.

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Anomaly Detection for Internet of Things Time Series Data Using Generative Adversarial Networks With Attention Mechanism in Smart Agriculture

Cited by 21 publications

References 37 publications

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

Multi-Crop Navigation Line Extraction Based on Improved YOLO-v8 and Threshold-DBSCAN under Complex Agricultural Environments

AgriSen-COG, a Multicountry, Multitemporal Large-Scale Sentinel-2 Benchmark Dataset for Crop Mapping Using Deep Learning

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