Anomaly detection in smart agriculture systems

Catalano, Christian; Paiano, Luigi; Calabrese, Francesca; Cataldo, M.; Mancarella, L.; Tommasi, Franco

doi:10.1016/j.compind.2022.103750

Cited by 27 publications

(11 citation statements)

References 23 publications

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“…In the training phase of both MLR and LSTM models, the datasets were obtained from Google Colab platform and the performance was evaluated by metrics. Right after the training process, the testing phase took place to generate predictions on the obtained data; therefore, the result will be assessed to reveal the detected anomalies [150]. The novelty of Akhter et al work is that they developed an interdigital phosphate sensor for smart agriculture with a lowcost and low-power planar.…”

Section: The Proposed Technique Was Implemented and Appliedmentioning

confidence: 99%

Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

et al. 2023

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Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers' profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.

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Section: The Proposed Technique Was Implemented and Appliedmentioning

confidence: 99%

Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

et al. 2023

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“…Swarming, young queens to signal readiness for battle with the mature queen [37] Queen quacking 200-350 quacking follows tooting, confined queens responses [40] Tremble 300-450 foragers returning to the hive, stimulates additional bees to function as nectar receivers [41] Waggle 250-300 recruitment to feeding sites, inform nestmates about direction and distance to locations of attractive food [42] Tooting 200-350, 400-500 young queens to signal readiness for battle with the mature queen [43] Worker piping 100-250, 330-430 Liftoff [36], [44] Low signals 0-100 indicators of worker bees activity level [45] Note that, the sensor peaks up the collective vibrational pattern of possibly, thousands of bees, and therefore, it is not a straightforward task to pinpoint a specific behavioral audio-signal. There is some controversy in the literature over the role of the vibrational pulses of main interest to our present study and sometimes, over the frequency range.…”

Section: Audio Signal Freq Bands (Hz)mentioning

confidence: 99%

A Low-cost, Low-power, Multisensory Device and Multivariable Timeseries Prediction for Beehive Health Monitoring.

Rigakis¹,

Potamitis²,

Tatlas³

et al. 2022

Preprint

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We present a custom platform that integrates data from several sensors measuring synchronously different variables of the beehive and wirelessly transmits all measurements to a cloud server. There is a rich literature on beehive monitoring. The choice of our work is not to use ready platforms such as Arduino and Raspberry Pi and to present a low cost and power solution for long term monitoring. We integrate sensors that are not limited to the typical toolbox of beehive monitoring such as gas, vibrations and bee counters. The synchronous sampling of all sensors every 5 minutes allows us to form a multivariable timeseries that serves two-ways: a) it provides immediate alerting in case a measurement exceeds predefined boundaries that are known to characterize a healthy beehive, and b) based on historical data predict future levels that are correlated with hive’s health. Finally, we demonstrate the benefit of using additional regressors in the prediction of the variables of interest. The database, the code and a video of the vibrational activity of two months are made open to the interested readers.

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“…With the swift advancement of computer technology and the enhancement of computational capabilities, the performance of deep learning-based anomaly detection techniques has been continuously improving. These techniques have found extensive applications in various domains, including agricultural production [ 1 , 2 ], industrial manufacturing [ 3 , 4 ], aerospace [ 5 , 6 ], and computer network security [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Industrial Product Surface Anomaly Detection with Realistic Synthetic Anomalies Based on Defect Map Prediction

Peng,

Zheng,

Zhao

et al. 2024

Sensors

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The occurrence of anomalies on the surface of industrial products can lead to issues such as decreased product quality, reduced production efficiency, and safety hazards. Early detection and resolution of these problems are crucial for ensuring the quality and efficiency of production. The key challenge in applying deep learning to surface defect detection of industrial products is the scarcity of defect samples, which will make supervised learning methods unsuitable for surface defect detection problems. Therefore, it is a reasonable solution to use anomaly detection methods to deal with surface defect detection. Among image-based anomaly detection, reconstruction-based methods are the most commonly used. However, reconstruction-based approaches lack the involvement of defect samples in the training process, posing the risk of a perfect reconstruction of defects by the reconstruction network. In this paper, we propose a reconstruction-based defect detection algorithm that addresses these challenges by utilizing more realistic synthetic anomalies for training. Our model focuses on creating authentic synthetic defects and introduces an auto-encoder image reconstruction network with deep feature consistency constraints, as well as a defect separation network with a large receptive field. We conducted experiments on the challenging MVTec anomaly detection dataset and our trained model achieved an AUROC score of 99.70% and an average precision (AP) score of 99.87%. Our method surpasses recently proposed defect detection algorithms, thereby enhancing the accuracy of surface defect detection in industrial products.

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Anomaly detection in smart agriculture systems

Cited by 27 publications

References 23 publications

Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

A Low-cost, Low-power, Multisensory Device and Multivariable Timeseries Prediction for Beehive Health Monitoring.

Industrial Product Surface Anomaly Detection with Realistic Synthetic Anomalies Based on Defect Map Prediction

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