Titlebook: Information and Communication Technologies for Agriculture—Theme I: Sensors; Dionysis D. Bochtis,Maria Lampridi,Panos Pardalos Book 2022 T

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Proximal Sensing Sensors for Monitoring Crop Growthe of use-cases are described to illustrate the most common practices of using proximal sensing sensors for precision agriculture. The use-case from Estonia demonstrates hand-held proximal sensor usage for variable rate fertilization. The use-cases from Lithuania illustrate field-scale monitoring and mapping of soil characteristics.

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Experimental Performance Evaluation Techniques of LoRa Radio Modules and Exploitation for Agriculturnt is ideal for educational purposes or for usage by not very experienced personnel, in the open field (e.g., orchards), the greenhouses or the livestock farming environment. This chapter, apart from highlighting the software and hardware details of the proposed measuring system and from presenting

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Evaluating the Performance of a Simulated Softwarized Agricultural Wireless Sensor Networkared to scenario (ii) and 58% compared to scenario (i). The use of SDN resulted in a considerable improvement in latency, which is essential for this domain. The main implications of these results are discussed and their main limitations along with future works that could improve and expand on them.

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Smart Agriculture: A Low-Cost Wireless Sensor Network Approachs experimental runs led by other researchers. The results of both approaches are briefly presented and analyzed. The performance evaluation metrics used to outline their effectiveness are the Round Trip Time of a data packet and the packet load on every time step. A brief review of the key component

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Potential of Sentinel-2 Satellite and Novel Proximal Sensor Data Fusion for Agricultural Applicationfers to plants or the soil, at different wavelengths of the spectrum of light. In this study, a maize field was monitored on several dates in 2018 growing season using both the Plant-O-Meter measurements and Sentinel-2 imagery. By utilizing appropriate formulas and spectral channels, various vegetat

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Trends in Satellite Sensors and Image Time Series Processing Methods for Crop Phenology Monitoringtion using a new in-house developed toolbox, DATimeS. These techniques have been put into practice for homogeneous and heterogeneous demonstration landscapes over the United States. Time series of Difference Vegetation Index (DVI) were processed from two EO data sources: high spatial resolution Sent

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Drone Imagery in Support of Orchards Trees Vegetation Assessment Based on Spectral Indices and Deep ing the combined use of ML techniques with spectral VIs derived from visible cameras mounted on drones, to be used as a proxy to characterise vegetation health of individual trees in an orchard field. To accomplish the study objectives, several image processing methods were implemented to the acquir