Variable Management for Uniform Potato Yield Using Remote Sensing Images With Unmanned Helicopter

Yokobori, Jun; Niwa, Katsuhisa; Sugiura, Ryo; Noguchi, Noboru; Chiba, Yutaka

doi:10.13031/2013.17866

Cited by 3 publications

(2 citation statements)

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“…However, the spatial coverage of the handheld instruments was incomplete, which made it challenging to capture the yield variability of the entire field. In the early 21st century, several studies were conducted using non-destructive and convenient UAVs and satellites, which provided more comprehensive spatial coverage, for potato yield estimation ( Yokobori et al., 2004 ; Bala and Islam, 2009 ). Currently, the technology for RS-based potato yield prediction has significantly advanced with the emergence of new-generation platforms, sensors, and advanced algorithms.…”

Section: Remote Sensing For Potato Yield Predictionmentioning

confidence: 99%

Methodological evolution of potato yield prediction: a comprehensive review

Lin,

Li,

Duan

et al. 2023

Front. Plant Sci.

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Timely and accurate prediction of crop yield is essential for increasing crop production, estimating planting insurance, and improving trade benefits. Potato (Solanum tuberosum L.) is a staple food in many parts of the world and improving its yield is necessary to ensure food security and promote related industries. We conducted a comprehensive literature survey to demonstrate methodological evolution of predicting potato yield. Publications on predicting potato yield based on methods of remote sensing (RS), crop growth model (CGM), and yield limiting factor (LF) were reviewed. RS, especially satellite-based RS, is crucial in potato yield prediction and decision support over large farm areas. In contrast, CGM are often utilized to optimize management measures and address climate change. Currently, combined with the advantages of low cost and easy operation, unmanned aerial vehicle (UAV) RS combined with artificial intelligence (AI) show superior potential for predicting potato yield in precision management of large-scale farms. However, studies on potato yield prediction are still limited in the number of varieties and field sample size. In the future, it is critical to employ time-series data from multiple sources for a wider range of varieties and large field sample sizes. This study aims to provide a comprehensive review of the progress in potato yield prediction studies and to provide a theoretical reference for related research on potato.

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Section: Remote Sensing For Potato Yield Predictionmentioning

confidence: 99%

Methodological evolution of potato yield prediction: a comprehensive review

Lin,

Li,

Duan

et al. 2023

Front. Plant Sci.

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“…Common vision-related robotic helicopter tasks in agriculture include remote sensing, spraying, and monitoring of crops and livestock for better farm management [24,25,26,27]. The tasks typically use onboard cameras to observe the agricultural objects in the field.…”

Section: Problem Descriptionmentioning

confidence: 99%

Markerless Augmented Reality for Robotic Helicoptor Applications

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Abstract. The objective of this research is to apply markerless Augmented Reality (AR) techniques to aid in the visualisation of robotic helicopter related tasks. Conventional robotic AR applications work well with markers in prepared environments but are infeasible in outdoor settings. In this paper, we present preliminary results from a real time markerless AR system for tracking natural features in an agricultural scene. By constructing a virtual marker under a known initial configuration of the robotic helicopter, camera and the ground plane, the camera pose can be continuously tracked using the natural features from the image sequence to perform augmentation of virtual objects. The experiments are simulated on a mock-up model of an agricultural farm and the results show that the current AR system is capable of tracking the camera pose accurately for translational motions and roll rotations. Future work includes reducing jitter in the virtual marker vertices to improve camera pose estimation accuracy for pitch and yaw rotations, and implementing feature recovery algorithms.

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