Augmented Perception for Agricultural Robots Navigation

Rovira-Más, Francisco; Sáiz-Rubio, Verónica; Cuenca-Cuenca, Andrés

doi:10.1109/jsen.2020.3016081

Cited by 46 publications

(23 citation statements)

References 15 publications

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“…Figure 5 a illustrates this phenomenon for several rows with a length longer than 100 m, when the robot of Section 3 followed a regular pattern along parallel rows. The plot in Figure 5 a compares the heading values read directly from the VTG NMEA string of a GPS receiver previously validated in the field [ 23 ], with the heading measured in real time with an electronic compass fixed to the vehicle, whose outputs were easily checked, as the actual orientation of the rows is known and can be simply calculated from satellite images. Nevertheless, using an electronic compass is not as easy as setting up plug-and-play devices; it requires an in situ calibration given that electromagnetic interferences within the vehicle will alter its readings.…”

Section: Methodsmentioning

confidence: 99%

Sensing Architecture for Terrestrial Crop Monitoring: Harvesting Data as an Asset

Rovira-Más

Sáiz-Rubio

Cuenca-Cuenca

2021

Sensors

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Very often, the root of problems found to produce food sustainably, as well as the origin of many environmental issues, derive from making decisions with unreliable or inexistent data. Data-driven agriculture has emerged as a way to palliate the lack of meaningful information when taking critical steps in the field. However, many decisive parameters still require manual measurements and proximity to the target, which results in the typical undersampling that impedes statistical significance and the application of AI techniques that rely on massive data. To invert this trend, and simultaneously combine crop proximity with massive sampling, a sensing architecture for automating crop scouting from ground vehicles is proposed. At present, there are no clear guidelines of how monitoring vehicles must be configured for optimally tracking crop parameters at high resolution. This paper structures the architecture for such vehicles in four subsystems, examines the most common components for each subsystem, and delves into their interactions for an efficient delivery of high-density field data from initial acquisition to final recommendation. Its main advantages rest on the real time generation of crop maps that blend the global positioning of canopy location, some of their agronomical traits, and the precise monitoring of the ambient conditions surrounding such canopies. As a use case, the envisioned architecture was embodied in an autonomous robot to automatically sort two harvesting zones of a commercial vineyard to produce two wines of dissimilar characteristics. The information contained in the maps delivered by the robot may help growers systematically apply differential harvesting, evidencing the suitability of the proposed architecture for massive monitoring and subsequent data-driven actuation. While many crop parameters still cannot be measured non-invasively, the availability of novel sensors is continually growing; to benefit from them, an efficient and trustable sensing architecture becomes indispensable.

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Section: Methodsmentioning

confidence: 99%

Sensing Architecture for Terrestrial Crop Monitoring: Harvesting Data as an Asset

Rovira-Más

Sáiz-Rubio

Cuenca-Cuenca

2021

Sensors

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“…The robot was powered by a stack of three electric Lithium-ion batteries supplying 24 VDC and 195 Ah. The navigation system was based on local perception [23] and combined a 3D stereo/TOF (O3M150, ifm electronic GmbH, Essen, Germany), a non-rotational LiDAR rangefinder (Multi-Ray LED Scanner OMD8000-R2100-R2-2V1, Pepperl + Fuchs, Mannheim, Germany), and four ultrasonic sensors (UC2000 30GM IUR2 V15, Pepperl + Fuchs, Mannheim, Germany). The central computing unit mounted in the robot was an industrial, fan-less computer that managed the sensors through a data acquisition card (NI USB-6216, National Instruments, Austin, TX, USA).…”

Section: Autonomous Ground Robotmentioning

confidence: 99%

Monitoring and Mapping Vineyard Water Status Using Non-Invasive Technologies by a Ground Robot

et al. 2021

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There is a growing need to provide support and applicable tools to farmers and the agro-industry in order to move from their traditional water status monitoring and high-water-demand cropping and irrigation practices to modern, more precise, reduced-demand systems and technologies. In precision viticulture, very few approaches with ground robots have served as moving platforms for carrying non-invasive sensors to deliver field maps that help growers in decision making. The goal of this work is to demonstrate the capability of the VineScout (developed in the context of a H2020 EU project), a ground robot designed to assess and map vineyard water status using thermal infrared radiometry in commercial vineyards. The trials were carried out in Douro Superior (Portugal) under different irrigation treatments during seasons 2019 and 2020. Grapevines of Vitis vinifera L. Touriga Nacional were monitored at different timings of the day using leaf water potential (Ψl) as reference indicators of plant water status. Grapevines’ canopy temperature (Tc) values, recorded with an infrared radiometer, as well as data acquired with an environmental sensor (Tair, RH, and AP) and NDVI measurements collected with a multispectral sensor were automatically saved in the computer of the autonomous robot to assess and map the spatial variability of a commercial vineyard water status. Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r2cv) of 0.57 in the morning time and a r2cv of 0.42 in the midday. The root mean square error of cross-validation (RMSEcv) was 0.191 MPa and 0.139 MPa at morning and midday, respectively. Spatial–temporal variation maps were developed at two different times of the day to illustrate the capability to monitor the grapevine water status in order to reduce the consumption of water, implementing appropriate irrigation strategies and increase the efficiency in the real time vineyard management. The promising outcomes gathered with the VineScout using different sensors based on thermography, multispectral imaging and environmental data disclose the need for further studies considering new variables related with the plant water status, and more grapevine cultivars, seasons and locations to improve the accuracy, robustness and reliability of the predictive models, in the context of precision and sustainable viticulture.

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“…Based on the development of the Internet of Things and wireless sensor networks (Yang et al, 2020;Friha et al, 2021), image gathering is becoming easier in the agricultural field. Moreover, smart applications based on agricultural images have been widely emerging in many aspects of agriculture, such as plant disease identification (Nagasubramanian et al, 2019;, crop pest recognition (Ayan et al, 2020;Liu and Wang, 2020;Mandal et al, 2021;Wang et al, 2021), fruits identification (Gao et al, 2020;Fu et al, 2021), yield forecasting (Schauberger et al, 2020;Shahhosseini et al, 2020;Jarlan et al, 2021), vision navigation (Kanagasingham et al, 2020;Rovira-Más et al, 2020;Emmi et al, 2021), and agricultural robot (Chen et al, 2020b;Guo et al, 2020;Wen et al, 2020;Zhang et al, 2020), etc. Although many remarkable achievements in the above typical aspects exist, the shortcomings of the current intelligent learning method are also revealed.…”

Section: Introductionmentioning

confidence: 99%

Toward Sustainability: Trade-Off Between Data Quality and Quantity in Crop Pest Recognition

Chao

2021

Front. Plant Sci.

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The crop pest recognition based on the convolutional neural networks is meaningful and important for the development of intelligent plant protection. However, the current main implementation method is deep learning, which relies heavily on large amounts of data. As known, current big data-driven deep learning is a non-sustainable learning mode with the high cost of data collection, high cost of high-end hardware, and high consumption of power resources. Thus, toward sustainability, we should seriously consider the trade-off between data quality and quantity. In this study, we proposed an embedding range judgment (ERJ) method in the feature space and carried out many comparative experiments. The results showed that, in some recognition tasks, the selected good data with less quantity can reach the same performance with all training data. Furthermore, the limited good data can beat a lot of bad data, and their contrasts are remarkable. Overall, this study lays a foundation for data information analysis in smart agriculture, inspires the subsequent works in the related areas of pattern recognition, and calls for the community to pay more attention to the essential issue of data quality and quantity.

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Augmented Perception for Agricultural Robots Navigation

Cited by 46 publications

References 15 publications

Sensing Architecture for Terrestrial Crop Monitoring: Harvesting Data as an Asset

Sensing Architecture for Terrestrial Crop Monitoring: Harvesting Data as an Asset

Monitoring and Mapping Vineyard Water Status Using Non-Invasive Technologies by a Ground Robot

Toward Sustainability: Trade-Off Between Data Quality and Quantity in Crop Pest Recognition

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