Fundació URV

Automatic feature descriptor in agriculture images using neural networks

Posted by Unitat from Fundació URVResponsive · Innovative Products and Technologies · Spain

Summary of the technology

Software that recognizes user-defined objects in aerial images for agriculture and derives quantitative estimators for plant count (N), green area index (GAI), growth stage (GS), deficient leaf area fraction (DLAF), and weeds area fraction (WAF), and potentially further measures. It employs a pre-trained multi-layer neural network model to classify regions of an image. The network is trained based on good examples of distinct types of objects that are provided by experts in aerial photography for agriculture. Afterwards, any provided aerial image or video can be analyzed with the pretrained software trained once.

Description of the technology

The network is trained based on good examples of distinct types of objects that are provided by experts in aerial photography for agriculture. Afterwards, any provided aerial image or video can be analyzed with the pretrained software trained once. Our implementation of the neuronal network model on Graphics Processing Units (GPU) provides a fast inference of the features in the images. The recognized features are added to the original image as an augmentation, the quantitative analysis (N, GAI, GS, DLAF, WAF) is performed on the fly based on a specialized and optimized set of postprocessing filters, and results are presented to the user employing comma-separated value tables. The software has the potential to replace the manual counting and evaluation by eye of aerial photos. Based on the postprocessing including a live statistical analysis employing local density and density correlations, the software may highlight abnormal regions and facilitate the user to find them. Another perspective of the software is the live computational taxonomy and automatic recognition of deficient areas and predict the growth stage. For instance, by applying logical rules on the spatial relationship between structural components the software may distinguish between crops and weed on the fly and automatically without an expert.

Technology Owner

Fundació URV

Technology Transfer Office

Related keywords

  • Agriculture and Marine Resources
  • Agriculture
  • Agriculture Machinery / Technology
  • Crop Production
  • Precision agriculture
  • Biocontrol
  • Horticulture
  • Imaging, Image Processing, Pattern Recognition
  • Agrofood Industry
  • Technologies for the food industry
  • Food Technology
  • Sylviculture, Forestry, Forest technology
  • Description Image/Video Computing
  • Electronics, IT and Telecomms
  • Information Processing, Information System, Workflow Management
  • Knowledge Management, Process Management
  • Industrial Technologies
  • Measurements and Standards
  • Measurement Tools
  • Recording Devices
  • neural networks

About Fundació URV

Technology Transfer Office from Spain

The Technology Transfer and Innovation Center (CTTi) meets from the University environment the technological needs and services generated by the productive sectors and administration, through the management of Transfer of Technology and Knowledge, the Intellectual and Intellectual Property management, Technology Watch, Entrepreneurship, and Technology Infrastructures Offer (business incubator).

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