Drone mapping software for precision agriculture
Photogrammetry software for agriculture. Converts drone images into orthomosaics, digital surface models, index maps, zones and prescription maps.
Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture
Precision farmers worldwide
Direct sales through webpage
Type of visuals supported (maps, charts, heat maps, layers, other)
Resource management, M&E, survey for project manegement (baseline, midline, and endline), household surveys for Health, Ag, financial inclusion, water, education,other, academic research survey, impact evaluations, market research, other (specify)
Specify the type of data collected by this service [Text, Numbers, Multimedia (Photos, video, audio, etc), Location, Other]
Mobile app data collection, remote sensing, government census data, etc.
Is the data from this application available?
Supported formats (JSON, XLS, CSV, KML, XML, other)
Telecommunication service required for the product/service to work (Mobile data, SMS, voice, Internet, Other)
Devices required for the product/service to work. (smartphone ,feature phone, computer, tablet, other [specify])
- Supports image import from any standard RGB cameras and several multispectral cameras like Parrot Sequoia and MicaSense RedEdge
- Supports import of field boundaries from farming management platforms, or directly input
- Supports import of orthomosaics, DSMs and index maps
- Instant mapping with no internet required
- Vegetation indices supporterd: NDVI, NDRE, VARI, TGI, SIPI2, LCI, BNDVI, GNDVI
- Supports split and double screen for interpretation and comparison
- Supports aggregation of information from the vegetation index maps into zones and assign application rates
- Outputs: Orthomosaic, Field boundaries, Vegetation index maps, Zonation maps, Prescription maps, Annotations
Pix4D: online and phone
Accuracy: consistent and comparable maps throughout the season
Instant results: generation of high-resolution maps while in the field
Agriculture intuitive: layer comparison, zonation and prescription tools
Drones are required to capture the images that feed the software
- T. Hovhannisyan, P. Efendyan, M. Vardanyan: Creation of a digital model of fields with application of DJI phantom 3 drone and the opportunities of its utilization in agriculture, Annals of Agrarian Science, Volume 16, Issue 2, 2018, Pages 177-180, ISSN 1512-1887
- Barrero, O. & Perdomo: RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields, Precision Agric (2018).
- Mitch S Maguire, Wayne E Woldt, Christopher M.U. Neale, Eric W Frew, George E Meyer: A Survey of Agricultural Image Processing for Unmanned Aircraft Systems, 2017. ASABE Annual International Meeting
- Chenghai Yang, Charles P. C. Suh, John K. Westbrook, “Early identification of cotton fields using mosaicked aerial multispectral imagery,” J. Appl. Rem. Sens. 11(1) 016008 (12 January 2017)
- O. Barrero, D. Rojas, C. Gonzalez and S. Perdomo, “Weed detection in rice fields using aerial images and neural networks“, 2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA), Bucaramanga, 2016, pp. 1-4.
Field tests with different organizations
Pix4Dfields knowledge base
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