Use of real-time extend GNSS for planting and inverting peanuts

Santos, Adão Felipe dos; Silva, Rouverson Pereira da; Zerbato, Cristiano; Menezes, Patricia Candida de; Kazama, Elizabeth Haruna; Paixão, Carla Segatto Strini; Voltarelli, Murilo Aparecido

doi:10.1007/s11119-018-9616-z

Cited by 22 publications

(9 citation statements)

References 19 publications

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“…Certain researchers attempted to validate the GNSS accuracy in agricultural areas in different areas worldwide (Pérez-Ruiz, Carballido, Agüera, & Gil, 2011;Guo et al, 2018;Catania et al, 2020). In an open-sky environment such as a paddy rice field, the GNSS can help realise highly accurate positioning (Santos et al, 2019). In contrast, in an orchard or mountainous areas, the GNSS positioning accuracy is affected by the presence trees.…”

Section: Introductionmentioning

confidence: 99%

Automatic travelling of agricultural support robot for a fruit farm -Verification of effectiveness of RTK-GNSS and developed simulator for specification design

2023

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Labour shortages and fatal accidents in agricultural work have recently emerged as critical problems in Japan, necessitating productivity enhancement, workload reduction, and safety assurance. Therefore, in Japan and countries with similar agricultural environments, the use of small and inexpensive agricultural robots that can be used in mountain farms and orchards is desirable. In this study, a dynamic positioning test was performed in orchards in a mountainous region to verify the positioning accuracy and stability of the Global Navigation Satellite System (GNSS) and real time kinematic (RTK)-GNSS. In addition, a simulator for an agricultural robot that could consider the environmental information of orchards was developed, and driving tests were conducted using the GNSS data acquired in the simulation. The error of the GNSS module was set to be higher than that for the measured value, and robot travelling in the orchard was simulated. The results of GNSS positioning tests in an orchard near a mountainous area indicate that in the specific environmental conditions, the RTK-GNSS and stand-alone (SA)-GNSS can attain a positioning accuracy with an order of tens of centimetres and few metres, respectively. Moreover, the simulation results based on the GNSS positioning results indicate that a vehicle implementing RTK-GNSS and a simple obstacle detection sensor can travel autonomously in a farmyard without colliding with the tree rows. In contrast, a vehicle implementing SA-GNSS and a simple obstacle detection sensor cannot drive autonomously in an orchard and must realise self-positioning using a more accurate sensor. Therefore, the proposed approach of realising simulations of autonomous agricultural robots based on GNSS data from a real orchard can facilitate the evaluation of practical agricultural robots and confirming safety traveling root. The results demonstrate the possibility of development of small agricultural robot for orchards. We conducted the GNSS positioning test in an orchard at an altitude of approximately 830 m, and a similar performance can be expected under similar agricultural situations because the error of the GNSS module was set to be higher than the measured value in driving simulation test.

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Section: Introductionmentioning

confidence: 99%

Automatic travelling of agricultural support robot for a fruit farm -Verification of effectiveness of RTK-GNSS and developed simulator for specification design

2023

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“…Despite being a new approach in the context of precision agriculture, statistical process control (SPC), has presented satisfactory results in monitoring the quality of agricultural processes (Santos et al., 2018; Tavares et al., 2018; Zerbato et al., 2017), helping to identify the causes of variability, patterns, and errors over time, making it possible to correct them. To verify the performance of agricultural operations, one must take into account critical factors that are difficult to identify.…”

Section: Introductionmentioning

confidence: 99%

Unmanned aerial system and satellite: Which one is a better platform for monitoring of the peanut crops?

et al. 2023

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Remote sensing tools are helpful in monitoring and managing crop production. However, each remote sensing technology responds to crop variability differently. In this way, the objective of this work was to compare sensors on airborne and orbital platforms and to observe which one has the best quality to determine the behavior of the peanut (Arachis hypogaea L.) crop variability. The experimental design followed the premises of the statistical quality control (SQC), with samples collected over time. The experimental area was composed of 30 sampling points spaced every 50 m. The multispectral images were acquired with an unmanned aerial system (UAS) consisting of a DJI Matrice quad-copter and a Micasense RedEdge multispectral camera and with the PlanetScope multispectral imaging satellites. It was verified that in all periods evaluated for spectral bands and vegetation indices (VI), satellite images presented better process quality. The enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) generated from satellite images were able to detect the peanut maturation variation better. The behavior of the bands and the VIs generated from the Planet images show quality for peanut crop monitoring. While UAS showed sensitivity to detect the saturation of the bands, making it difficult to visualize the temporal variability.

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“…Precision agriculture (PA) is a farming management concept based on observing, measuring and responding to the spatiotemporal variability in weather, soil and agricultural production (Guo, et al, 2018). It uses indirect methods, as Red-Green-Near infrared (RG-NIR) sensors (Dos Santos, Filgueiras, Mantovani, FernandesFilho, & Almeida, 2021), electromagnetic induction (EMI) sensors (Rudolph, Marchant, Weihermüller, & Vereecken, 2019) or GNSS (Dos Santos, et al, 2019) for many applications, measuring soil moisture (Jia & Savi, 2017) or soil classes (Hubbard, et al, 2021), which means a signi cant increase in productivity.…”

Section: Introductionmentioning

confidence: 99%

GNSS-IR applied to vineyard moisture

Garrido-Villén

Berné

2022

Preprint

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Multipath is one of the most severe station-dependent error sources in static GNSS positioning, yet it can be used to conduct several studies. The Global Navigation Satellite System – interferometry and reflectometry (GNSS-IR) technique has been widely used to study soil moisture and snow depth. In this work, GNSS-IR is used to find the correlation between vineyard leaf moisture and GNSS signal-to-noise ratio (SNR). The GNSS data have been collected during the leafy period in a vineyard when water content varies significantly. Only data from low satellite elevation angles (between 5º and 30º) have been taken into consideration and the azimuth has been studied because trellis plantation was used in the vineyard. The amplitudes of the SNR data correlate with the water content in vineyard leaves. Both GPS and Galileo signals were evaluated, but a lack of signals prevented full use of the Galileo system. Our study shows that the L2C signal is better for the analysis of humidity as there is a greater correlation. Finally, the results show that with the latest GPS, L2C, and L5 signals, the SNR can be used more reliably for multipath reflectometry. GPS L5 signal will become more important because of the increasing compatibility and interoperability of global satellite navigation systems.

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Use of real-time extend GNSS for planting and inverting peanuts

Cited by 22 publications

References 19 publications

Automatic travelling of agricultural support robot for a fruit farm -Verification of effectiveness of RTK-GNSS and developed simulator for specification design

Automatic travelling of agricultural support robot for a fruit farm -Verification of effectiveness of RTK-GNSS and developed simulator for specification design

Unmanned aerial system and satellite: Which one is a better platform for monitoring of the peanut crops?

GNSS-IR applied to vineyard moisture

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