Updated on June 15, 2020


Created on October 8, 2018


Upcoming Update

Drone mapping software for aerial crop analysis and precision agriculture.

Developed By Unknown
Content Partners


Product Description

Pix4Dfields is a photogrammetry software designed for precision agriculture and crop analysis.  It digitizes fields by converting drone images into orthomosaics, digital surface models, index maps, zones and prescription maps allowing maximization of operational efficiency.

Market Suggested Retail Price


Distributors / Implementing Organizations

Manufacturing/Building Method

Software Development

Intellectural Property Type


User Provision Model

Direct sales through webpage

Distributions to Date Status


Design Specifications

  • 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

Technical Support

Pix4D: online and phone

Replacement Components




Manufacturer Specified Performance Parameters

Accuracy: consistent and comparable maps throughout the season Instant results: generation of high-resolution maps while in the field, without internet connection Agriculture intuitive: layer comparison, zone and prescription tools Collaboration: upload and access information between team members

Vetted Performance Status

Acquisition of plot statistical information (NDVI mean, median, stdev and min & max) from indices, helped understand the impact of different agricultural techniques over the crops. Pix4Dfield was updated with radiometric corrections, allowing to map bigger areas while reducing processing time. This reduces waiting times from several hours to minutes.



Complementary Technical Systems

Drones are required to capture the images that feed the software

Academic Research and References

Hovhannisyan, T., Efendyan, P., Vardanyan, M., 2018, 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, , ISSN 1512-1887, Pages 177-180

Barrero, O.,  Perdomo, S., 2018, RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields, Precision Agric

Maguire, M., Woldt, W., Neale,  C., Frew, E., Meyer, G., 2017, A Survey of Agricultural Image Processing for Unmanned Aircraft Systems,  ASABE Annual International Meeting

Yang, C., Suh, C., Westbrook, J., 2017, Early identification of cotton fields using mosaicked aerial multispectral imagery, J Appl Rem Sens 11(1) 016008

Barrero, O., Rojas, D., Gonzalez, C.,  2016, Weed detection in rice fields using aerial images and neural networks, 2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA), Bucaramanga,  pp. 1-4

Ihsan, M., Somantri, L., Sugito, N., Himayah, S., Affriani, A., 2018, The Comparison of Stage and Result Processing of Photogrammetric Data Based on Online Cloud ProcessingIOP Conference Series: Earth and Environmental ScienceVolume 286IGEOS International Geography Seminar, Malaysia

Compliance with regulations


Other Information

Pix4Dfields knowledge base

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