{"type": "FeatureCollection", "features": [{"id": "10.5220/0009169301030110", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:22:54Z", "type": "Journal Article", "created": "2020-03-19", "title": "Two-step Multi-spectral Registration Via Key-point Detector and Gradient Similarity: Application to Agronomic Scenes for Proxy-sensing", "description": "The potential of multi-spectral images is growing rapidly in precision agriculture, and is currently based on the use of multi-sensor cameras. However, their development usually concerns aerial applications and their parameters are optimized for high altitudes acquisition by drone (UAV \u2248 50 meters) to ensure surface coverage and reduce technical problems. With the recent emergence of terrestrial robots (UGV), their use is diverted for nearby agronomic applications. Making it possible to explore new agronomic applications, maximizing specific traits extraction (spectral index, shape, texture \u2026) which requires high spatial resolution. The problem with these cameras is that all sensors are not aligned and the manufacturers\u2019 methods are not suitable for close-field acquisition, resulting in offsets between spectral images and degrading the quality of extractable informations. We therefore need a solution to accurately align images in such condition. In this study we propose a two-steps method applied to the six-bands Airphen multi-sensor camera with (i) affine correction using pre-calibrated matrix at different heights, the closest transformation can be selected via internal GPS and (ii) perspective correction to refine the previous one, using key-points matching between enhanced gradients of each spectral bands. Nine types of key-point detection algorithms (ORB, GFTT, AGAST, FAST, AKAZE, KAZE, BRISK, SURF, MSER) with three different modalities of parameters were evaluated on their speed and performances, we also defined the best reference spectra on each of them. The results show that GFTT is the most suitable methods for key-point extraction using our enhanced gradients, and the best spectral reference was identified to be the band centered on 570 nm for this one. Without any treatment the initial error is about 62 px, with our method, the remaining residual error is less than 1 px, where the manufacturer\u2019s involves distortions and loss of information with an estimated residual error of approximately 12 px", "keywords": ["03 medical and health sciences", "0302 clinical medicine", "Registration", "Registration", " Multi-spectral imagery", " Precision farming", " Feature descriptor", "0202 electrical engineering", " electronic engineering", " information engineering", "[SDV.SA.STA] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture", "Precision farming", "Feature descriptor", "Multi-spectral imagery", "02 engineering and technology", "15. Life on land", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing"]}, "links": [{"href": "https://doi.org/10.5220/0009169301030110"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%2015th%20International%20Joint%20Conference%20on%20Computer%20Vision%2C%20Imaging%20and%20Computer%20Graphics%20Theory%20and%20Applications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5220/0009169301030110", "name": "item", "description": "10.5220/0009169301030110", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5220/0009169301030110"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.2620125", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:24:18Z", "type": "Journal Article", "title": "GNSS PRECISE POINT POSITIONING FOR AUTONOMOUS ROBOT NAVIGATION IN GREENHOUSE ENVIRONMENT FOR INTEGRATED PEST MONITORING", "description": "GreenPatrol robot is an autonomous robotic solution for early detection and control of pests in greenhouses. The importance of robot precise positioning inside the greenhouse is a key aspect to endow the robot with the ability to scout the environment, precisely register the detected pest location into accurate maps and to allow the later treatment. Greenhouses are a challenging environment in terms of multipath and signal blockage due to its metal-reinforced complex structures of glass or polycarbonate. GreenPatrol robot localization takes advantage of the higher accuracy and the multiple signal frequencies provided by the European Global Navigation Satellite System (EGNSS) of the Galileo constellation (E5Alt BOC), by means of precise positioning techniques combined with inertial measurement sensors, odometry and maps to provide an accurate global localization mechanism.<br> This paper shows the results of a comparative analysis carried out in a Greenhouse environment in order to evaluate the  performance of different processing techniques such as Precise Point Positioning (PPP) and Post Processed Kinematic (PPK). The purpose of this analysis is to study the advantages of the use of Galileo new signals and to determine the best global localization solution for the GreenPatrol robot. The results experimentally show that the use of PPP Galileo E5 AltBOC signal in a multi-constellation solution offers better signal quality and better positioning performance for the intended environment.", "keywords": ["13. Climate action", "GNSS", " Precise Point Positioning", " Precision Farming", " Integrated Pest Monitoring"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.2620125"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/12%20th%20Annual%20Ba%C5%A1ka%20GNSS%20Conference%20Proceedings", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.2620125", "name": "item", "description": "10.5281/zenodo.2620125", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.2620125"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=+Precision+farming&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=+Precision+farming&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=+Precision+farming&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=+Precision+farming&offset=2", "hreflang": "en-US"}], "numberMatched": 2, "numberReturned": 2, "distributedFeatures": [], "timeStamp": "2026-07-27T16:59:21.675018Z"}