Devices, systems, and methods for automated monitoring enabling precision agriculture

Das, Jnaneshwar; Cross, Gareth; Qu, Chao; Makineni, Anurag; Tokekar, Pratap; Mulgaonkar, Yash; Kumar, Vijay

doi:10.1109/coase.2015.7294123

Cited by 130 publications

(81 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Precision agriculture is optimizing application of geospatial analysis Satellites can be used to take pictures of the crops, high resolution imagery has been used in some fields (Zhang and Kovacs, 2012) but even then the satalite images cannot provide the resolution requiered for the detailed analysis that is needed for some application in precision agriculture. As a solution, quadrotors has been used with measurement sensors mounted on them for detailed analysis of crops (Das et al, 2015).…”

Section: Precision Agriculturementioning

confidence: 99%

“…One of the rising technologies used for agricultural inspections is the use of unmaned air vehicles (UAVs) which are used to take aerial pictures of the farms so that the images could be processed to extract data about the state of the crops (Das et al, 2015). For this process both fixed wings and quadrotors UAVs are used with a preference over the quadrotor since it's easier to operate and has a milder learning curve compared to fixed wings (Kolodny, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Swarm Robotics as a Solution to Crops Inspection for Precision Agriculture

Carbone

Garibaldi

Kurt

2018

KEG

View full text Add to dashboard Cite

This paper summarizes the concept of swarm robotics and its applicability to crop inspections. To increase the agricultural yield it is essential to monitor the crop health.Hence, precision agriculture is becoming a common practice for farmers providing a system that can inspect the state of the plants ( Khosla and others, 2010). One of the rising technologies used for agricultural inspections is the use of unmaned air vehicles (UAVs) which are used to take aerial pictures of the farms so that the images could be processed to extract data about the state of the crops (Das et al., 2015). For this process both fixed wings and quadrotors UAVs are used with a preference over the quadrotor since it's easier to operate and has a milder learning curve compared to fixed wings (Kolodny, 2017). UAVs require battery replacement especially when the environmental conditions result in longer inspection times ("Agriculture -Maximize Yields with Aerial Imaging," n.d., "Matrice 100 -DJI Wiki," n.d.). As a result, inspection systems for crops using commercial quadrotors are limited by the quadrotor´s maximum flight speed, maximum flight height, quadrotor´s battery time, crops area, wind conditions, etc.("Mission Estimates," n.d.).

show abstract

Section: Precision Agriculturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Swarm Robotics as a Solution to Crops Inspection for Precision Agriculture

Carbone

Garibaldi

Kurt

2018

KEG

View full text Add to dashboard Cite

show abstract

“…For example, the rapid growth in the use of UAVs for aerial photogrammetry is having a big impact also in these scenarios Chiabrando et al, 2017;Fernandez Galarreta et al, 2015) creating new perspectives for the management of future events and new operation procedures. However, in this work the focus will be set on different types of sensors and considering a different scale (mainly architectural) and the aerial contribution will not be considered in this part of research.Different groups of researchers have already investigated the use of low-cost and COTS sensors in different scenarios (Chiabrando et al, 2017;Das et al, 2015;Kolev et al, 2014;Micheletti et al, 2015), however lot of questions are still open and the definition of operative standards in this field is still missing.…”

Section: Rapid Mapping Strategies With Low-cost and Cots Solutions Fomentioning

confidence: 99%

Rapid Mapping for Built Heritage at Risk Using Low-Cost and Cots Sensors. A Test in the Duomo Vecchio of San Severino Marche

Calantropio

Colucci

Losè

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:In the last years, the researchers in the field of Geomatics have focused their attention in the experimentation and validation of new methodologies and techniques, stressing especially the potential of low-cost and COTS (Commercial Off The Shelf) solutions and sensors. In particular, these tools have been used with purposes of rapid mapping in different contexts (ranging from the construction industry, environmental monitoring, mining activities, etc.). The Built Heritage, due to its intrinsic nature of endangered artefact, can largely benefit from the technological and methodological innovations in this research field. The contribute presented in this paper will highlight these main topics: the rapid mapping of the Built Heritage (in particular the one subjected to different types of risk) using low-cost and COTS solutions. Different sensors and techniques were chosen to be evaluated on a specific test site: the Duomo Vecchio of San Severino Marche (MC -Italy), that was partially affected by the earthquake swarm that hit the area of Central Italy starting from the 24 th of August 2016. One of the main aims of this work is to demonstrate how low-cost and COTS sensors can contribute to the documentation of the Built Heritage for its safeguard, for damage assessment in case of disastrous events and operations of restoration and preservation.

show abstract

“…Methods include a variety of platforms, such as manned and unmanned ground vehicles (UGVs) [4,5,6,7,8], unmanned aerial vehicles (UAVs) and hand-held sensors [9]. Different types of imaging sensors have also been used, including “standard” (visible light) cameras and stereo, near infra-red, long wave thermal infrared cameras and LiDAR [2,3,4,9,10,11,12,13,14]. Standard cameras are a common choice, due to their low cost, the richness and comparatively high resolution of the data they provide, as well as the familiarity to the machine vision community in terms of data acquisition and processing techniques [12].…”

Section: Introductionmentioning

confidence: 99%

Image Based Mango Fruit Detection, Localisation and Yield Estimation Using Multiple View Geometry

Stein

Bargoti

Underwood

2016

Sensors

237

146

View full text Add to dashboard Cite

This paper presents a novel multi-sensor framework to efficiently identify, track, localise and map every piece of fruit in a commercial mango orchard. A multiple viewpoint approach is used to solve the problem of occlusion, thus avoiding the need for labour-intensive field calibration to estimate actual yield. Fruit are detected in images using a state-of-the-art faster R-CNN detector, and pair-wise correspondences are established between images using trajectory data provided by a navigation system. A novel LiDAR component automatically generates image masks for each canopy, allowing each fruit to be associated with the corresponding tree. The tracked fruit are triangulated to locate them in 3D, enabling a number of spatial statistics per tree, row or orchard block. A total of 522 trees and 71,609 mangoes were scanned on a Calypso mango orchard near Bundaberg, Queensland, Australia, with 16 trees counted by hand for validation, both on the tree and after harvest. The results show that single, dual and multi-view methods can all provide precise yield estimates, but only the proposed multi-view approach can do so without calibration, with an error rate of only 1.36% for individual trees.

show abstract

Devices, systems, and methods for automated monitoring enabling precision agriculture

Cited by 130 publications

References 16 publications

Swarm Robotics as a Solution to Crops Inspection for Precision Agriculture

Swarm Robotics as a Solution to Crops Inspection for Precision Agriculture

Rapid Mapping for Built Heritage at Risk Using Low-Cost and Cots Sensors. A Test in the Duomo Vecchio of San Severino Marche

Image Based Mango Fruit Detection, Localisation and Yield Estimation Using Multiple View Geometry

Contact Info

Product

Resources

About