Multilevel data fusion for the internet of things in smart agriculture

Torres, Andrei; Rocha, Atslands Rego da; Silva, Ticiana L. Coelho da; Souza, José Neuman de; Gondim, Rubens Sonsol

doi:10.1016/j.compag.2020.105309

Cited by 42 publications

(21 citation statements)

References 21 publications

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“…The fusion of both pieces of the aforementioned information helps making crop and crop parameter-related decisions. Therefore, it can suggest the farmers about the precise quantity of water for irrigation purposes and avoid wasting the irrigation water due to bad irrigation management and strategies [136].…”

Section: Artificial Intelligence and Internet Of Things (Iot) Data Fu...mentioning

confidence: 99%

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

et al. 2022

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Digital farming approach merges new technologies and sensor data to optimize the quality of crop monitoring in agriculture. The successful fusion of technology and data is highly dependent on the parameter collection, the modeling adoption, and the technology integration being accurately implemented according to the specified needs of the farm. This fusion technique has not yet been widely adopted due to several challenges; however, our study here reviews current methods and applications for fusing technologies and data. First, the study highlights different sensors that can be merged with other systems to develop fusion methods, such as optical, thermal infrared, multispectral, hyperspectral, light detection and ranging and radar. Second, the data fusion using the internet of things is reviewed. Third, the study shows different platforms that can be used as a source for the fusion of technologies, such as ground-based (tractors and robots), space-borne (satellites) and aerial (unmanned aerial vehicles) monitoring platforms. Finally, the study presents data fusion methods for site-specific crop parameter monitoring, such as nitrogen, chlorophyll, leaf area index, and aboveground biomass, and shows how the fusion of technologies and data can improve the monitoring of these parameters. The study further reveals limitations of the previous technologies and provides recommendations on how to improve their fusion with the best available sensors. The study reveals that among different data fusion methods, sensors and technologies, the airborne and terrestrial LiDAR fusion method for crop, canopy, and ground may be considered as a futuristic easy-to-use and low-cost solution to enhance the site-specific monitoring of crop parameters.

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Section: Artificial Intelligence and Internet Of Things (Iot) Data Fu...mentioning

confidence: 99%

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

et al. 2022

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“…To deal with this issue, researchers provide various solutions to interface and integrate data coming from different sources. In [1], the authors introduce Hydra as an IoT multilevel data fusion system that fuses data at different layers, including raw sensor data, events and decision-making, and decision fusion based on applications, to support water management applications for cultivated fields. In another work [2], the authors propose an approach that exploits multivariate geostatistical data fusion techniques to fuse multi-temporal data from a multi-band radiometer and a geophysical sensor, to delineate homogeneous zones to be assigned to differential agricultural management.…”

Section: Related Workmentioning

confidence: 99%

“…Inspired by [10], the Data preparation and integration module implements a harmonization process to get an integrated dataset of images from Sentinel and Landsat satellites. The process consists in several steps, as reported in Figure 2, that are carried out by using Google Earth Engine (GEE) API 1 . As a first step, spectral images are acquired from Landsat 7, 8, and Sentinel-2, generally, satellite measurements are acquired at top-of-theatmosphere reflectance, which is a mix of light reflected off the surface of the Earth and off the atmosphere, therefore, spectral images require some atmospheric corrections [11] to get rid of the effect of the atmosphere on the reflectance values and get images showing only the actual reflectance of the areas on the surface of the Earth (bottom-of-atmosphere reflectance).…”

Section: A Data Preparation and Integrationmentioning

confidence: 99%

Incremental Knowledge Extraction From IoT-Based System for Anomaly Detection in Vegetation Crops

Cavaliere

Senatore

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Precision agriculture systems collect spectral images from satellites, from which vegetation indices (VIs) can be assessed to monitor vegetation and soil condition. It requires a neardaily data acquisition to perform robust crop monitoring and data analysis. Satellites provide a periodic data acquisition that need a further data integration using multiple satellite sources along with camera-equipped drones to achieve an accurate data collection on a selected area. Moreover, VIs are not enough for a proper vegetation evaluation of the monitored areas due to differences among cultivars, the phenological season in which the vegetation is evaluated, the latitude of the areas, etc. This paper introduces a system model to detect anomalies regarding the vegetation and soil conditions according to the area phenology and the historical vegetation trends. The system collects spectral images of the regions of interest (ROIs) from satellites and drones, harmonized to calculate VIs and feeds a dataset of neardaily high-resolution integrated images. The harmonic analysis allows phenological data extraction about the ROIs, hence the Territorial Observation Model (TOM) has been extended to represent phenological stages and build knowledge on the ROIs and their phenology that is stored on a triple store. The system selects the VI values, calculated during the learned growing seasons of the ROIs, and classifies them to detect vegetation anomalies affecting those ROIs. The collected knowledge can be used by end-users (e.g. agronomists, experts, etc.) to analyze the anomalies correlated to historical results and vegetation trends.

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“…Torres et al developed a three-layer data fusion algorithm, called Hydra, for improving the accuracy of employed sensors to determine some critical events more accurately, and thus for making appropriate decisions related to the smart water management [ 9 ]; the Hydra algorithm employs, at the lower level, some filtering methods (extreme Studentized method—ESD and weighted outlier–Robust Kalman filter—WRKF) for identifying and removing the outliners, thus enhancing the algorithm reliability.…”

Section: Scientific Work and Commercial Devices For Precision Farmentioning

confidence: 99%

Development of Sensors-Based Agri-Food Traceability System Remotely Managed by a Software Platform for Optimized Farm Management

Visconti

Fazio

Velázquez

et al. 2020

Sensors

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The huge spreading of Internet of things (IoT)-oriented modern technologies is revolutionizing all fields of human activities, leading several benefits and allowing to strongly optimize classic productive processes. The agriculture field is also affected by these technological advances, resulting in better water and fertilizers’ usage and so huge improvements of both quality and yield of the crops. In this manuscript, the development of an IoT-based smart traceability and farm management system is described, which calibrates the irrigations and fertigation operations as a function of crop typology, growth phase, soil and environment parameters and weather information; a suitable software architecture was developed to support the system decision-making process, also based on data collected on-field by a properly designed solar-powered wireless sensor network (WSN). The WSN nodes were realized by using the ESP8266 NodeMCU module exploiting its microcontroller functionalities and Wi-Fi connectivity. Thanks to a properly sized solar power supply system and an optimized scheduling scheme, a long node autonomy was guaranteed, as experimentally verified by its power consumption measures, thus reducing WSN maintenance. In addition, a literature analysis on the most used wireless technologies for agri-food products’ traceability is reported, together with the design and testing of a Bluetooth low energy (BLE) low-cost sensor tag to be applied into the containers of agri-food products, just collected from the fields or already processed, to monitor the main parameters indicative of any failure or spoiling over time along the supply chain. A mobile application was developed for monitoring the tracking information and storing conditions of the agri-food products. Test results in real-operative scenarios demonstrate the proper operation of the BLE smart tag prototype and tracking system.

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Multilevel data fusion for the internet of things in smart agriculture

Cited by 42 publications

References 21 publications

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

Incremental Knowledge Extraction From IoT-Based System for Anomaly Detection in Vegetation Crops

Development of Sensors-Based Agri-Food Traceability System Remotely Managed by a Software Platform for Optimized Farm Management

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