Precision farming in crop and livestock production

Photo: ATB

Cracking the data to combat fruit cracking

Laser scanner mounted at a circular conyeyor for automated scanning of fruit characteristics in the field (Photo: ATB)

ATB participates in the 7,5 mio Euro CrackSense project that will utilise sensing and data technologies to enhance fruit quality

Bringing together 14 partners across seven European countries, the new EU project CrackSense will address the fruit cracking challenge, focusing on real-time monitoring and prediction by utilising and upscaling sensing and digital data technologies. Since fruit cracking is a peel disorder, limiting fruit quality and yield, the ambition of the project is to monitor agri-environmental conditions because these conditions have a great influence on agricultural production.

CrackSense seeks to curb cracking in citrus, pomegranate, grapes and cherries by piloting and deploying sensor technologies in Israel, France, Germany, Lithuania and Greece to provide real-time data.

The phenomenon of fruit cracking occurs mainly in the pre-harvest stage, and it is often a physiological response to climatic and environmental conditions. It initiates at the surface of the fruit, where cracks traverse the skin and penetrate the inner tissues. That can result in yield loss in many fruit crops.

ATB is responsible for work package 2, focussing on proximal sensing of cracking-related fruit properties and online data provision by using light detection and range (LiDAR) laser scanner, thermal and RGB cameras and edge device. Data at fruit level that are not available so far, such as fruit wetness, are of particular interest to describe and early detect cracking on single fruits for mechanistic and deep learning models. The approach will be validated on fruit with varying skin thickness and colour such as pomegranate, citrus, and grapes as well as fruit growing in clusters such as sweet cherry.

The sensor data collected in the project will be combined with Earth Observation Data (provided by Copernicus, e.g., Sentinel satellite) and other data sets reflecting on environmental conditions. Biophysical and biochemical traits that control plant physiology will be monitored at large spatial scales by drones to optimise resource use and enhance fruit production. As CrackSense strives to collect diverse and punctual data sets across piloting regions, both remote and proximal sensing technologies will be used for these purposes.

 

By improving the agricultural production of the crops mentioned above, CrackSense will also help in creating a model for other agricultural crops and mitigation strategies for fruit cracking, which could result in up to 50% higher financial gain for farmers and growers.

The CrackSense project is funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No. GA No. 101086300. It is coordinated by The Agricultural Research Organisation of Israel - The Volvani Centre (ARO). More information is available on the project’s website https://cracksense.eu/  

Contact ATB: Dr. Manuela Zude-Sasse

Calendar file

Cookies

We use cookies. Some are required to offer you the best possible content and functions while others help us to anonymously analyze access to our website. (Matomo) Privacy policy

Required required

Necessary cookies are absolutely essential for the proper functioning of the website. This category only includes cookies that ensure basic functionalities and security features of the website. These cookies do not store any personal information.

Cookie Duration Description
PHPSESSID Session Stores your current session with reference to PHP applications, ensuring that all features of the site can be displayed properly. The cookie is deleted when the browser is closed.
bakery 24 hours Stores your cookie preferences.
fe_typo_user Session Is used to identify a session ID when logging into the TYPO3 frontend.
__Secure-typo3nonce_xxx Session Security-related. For internal use by TYPO3.
Analytics

With cookies in this category, we learn from visitors' behavior on our website and can make relevant information even more accessible.

Cookie Duration Description
_pk_id.xxx 13 months Matomo - User ID (for anonymous statistical analysis of visitor traffic; determines which user is being tracked)
_pk_ses.xxx 30 minutes Matomo - Session ID (for anonymous statistical analysis of visitor traffic; determines which session is being tracked)