UTFacultiesEEMCSDisciplines & departmentsPSEducationAssignment: Identifying the use of different camera lenses of a spectral camera in the agricultural sector

Assignment: Identifying the use of different camera lenses of a spectral camera in the agricultural sector

Identifying the use of different camera lenses of a spectral camera in the agricultural sector

Problem statement

Conventional RGB cameras capture images that are similar to what a human could see. Therefore, they are very usable, because as humans we are easily able to interpret the images and come up with pipelines for analysis. Nevertheless, plants emit and absorb more wavelengths than the ones which are collected in RGB; plants absorb solar radiation to execute their photosynthesis, which is subsequently partly re-emitted in the near-infrared (NIR) spectral region to prevent overheating. The NIR spectral region is not visible by humans, but can be captured by spectral cameras and subsequently used to estimate plant stress (caused by for example water shortage, pests, diseases, nutrient shortage, or heat stress). An interesting current trend is to investigate the potential of the NIR spectral region for automatically identifying plant stress and health.


The task of this project is to investigate which potential NIR spectral regions may offer, and what the limitations and advantages of these offers are. Subsequently, data analysis can be executed on data that is already collected in a greenhouse to investigate the outcomes of different analyses using the NIR spectral region. Also, an AI algorithm could be trained to automatically detect plant stress in an early stage. Subsequently, this AI algorithm can be used to inform the farmer about possible shortcomings in an early stage, which helps the farmer increasing his yield. 


20% Theory, 20% research potential NIR based on already collected data, 40% implementation, 20% writing report


Le Viet Duc, v.d.le@utwente.nl

About Track32:

At Track32 we produce innovative computer vision and AI software. Making the technology easily accessible, so that it becomes part of your organization’s natural intelligence.

Track32 provides state-of-the-art computer vision and AI algorithms, and we integrate them into existing or new hardware and software systems. We are experts in processing all types of visual and non-visual data, using deep learning and other methods. Track32 excels at analyzing the user’s requirements and matching them with technically feasible and cost-effective solutions. Using our computer vision and AI solutions leads to huge cost savings and massively increased operational efficiency for our customers.

Computer vision and AI are generic technologies that can be used in any application domain. We serve a wide spectrum of customers in the agricultural supply chain, but also players in other markets such as post harvest, material handling, spatial planning, healthcare and the life sciences. We serve commercial companies as well as research institutes and government.