Towards an Integrated Low-Cost Agricultural Monitoring System with Unmanned Aircraft System

Karatzinis, Georgios; Apostolidis, Savvas; Kapoutsis, Athanasios Ch.; Panagiotopoulou, Liza; Boutalis, Yiannis S.; Kosmatopoulos, Elias B.

doi:10.1109/icuas48674.2020.9213900

Cited by 14 publications

(11 citation statements)

References 30 publications

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“…Notice that due to the complex nature of the problem, the custom-built dataset is imbalanced, leading to a quite challenging detection task. Last but not least, the dataset, apart from the weed detection and identification, can be used for additional precision agriculture tasks, such as crop row detection [7] , yield estimation [8] , etc.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

CoFly-WeedDB: A UAV image dataset for weed detection and species identification

et al. 2022

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

CoFly-WeedDB: A UAV image dataset for weed detection and species identification

et al. 2022

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“…The segmentation technique that works on the histogram features of an image and helps to segment images containing bright objects or patches is known as thresholding [102]. This technique transforms a colored or grayscale image into a binary (black and white) image that helps in further processes and minimizes difficulties.…”

Section: Thresholding Segmentationmentioning

confidence: 99%

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Ali

Yang

Israr

et al. 2023

Drones

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Disease detection in plants is essential for food security and economic stability. Unmanned aerial vehicle (UAV) imagery and artificial intelligence (AI) are valuable tools for it. The purpose of this review is to gather several methods used by our peers recently, hoping to provide some knowledge and assistance for researchers and farmers so that they can employ these technologies more advantageously. The studies reviewed in this paper focused on Scab detection in Rosaceae family fruits. Feature extraction, segmentation, and classification methods for processing the UAV-obtained images and detecting the diseases are discussed briefly. The advantages and limitations of diverse kinds of UAVs and imaging sensors are also explained. The widely applied methods for image analysis are machine learning (ML)-based models, and the extensively used UAV platforms are rotary-wing UAVs. Recent technologies that cope with challenges related to disease detection using UAV imagery are also detailed in this paper. Some challenging issues such as higher costs, limited batteries and flying time, huge and complex data, low resolution, and noisy images, etc., still require future consideration. The prime significance of this paper is to promote automation and user-friendly technologies in Scab detection.

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“…A common one -still today -is to convert spectral information to vegetation indices (e.g., NDVI) and thereof resort to a semantic segmentation technique to identify pixels belonging to the vine plants. Early (classical) segmentation approaches were mainly based on thresholds (Karatzinis et al, 2020), color indices (Kirk et al, 2009), clustering (Comba et al, 2015), histograms (Hall et al, 2003) or classical supervised (Guerrero et al, 2012) and unsupervised (Comba et al, 2018) learning methods. Advantages of these classical approaches include simplicity, 'shallow' training, and low computation cost.…”

Section: Related Workmentioning

confidence: 99%

“…Deep Learning (DL) has been increasingly gaining relevance in precision agriculture, namely in remote sensing tasks. Remote sensing technology such as satellite and UAVs allow non-invasive and timeeffective inspection techniques, which enable the automation of tasks such as disease detection (Kerkech et al, 2020a), crop yield prediction (van Klompenburg et al, 2020), and other monitoring-related tasks (Karatzinis et al, 2020). Conversely to satellites, which are limited by temporal and resolution constraints, UAV-based remote sensing offers a cost-effective data collection approach to generate the necessary geospatial products of smaller crops such as vineyards (Deng et al, 2018).…”

Section: Introductionmentioning

confidence: 99%