Detection of fruit temperature threshold that will lead to irreversible sunburn damage

We can predict apple and grape fruit sunburn with the use of sensors and we can suggest ways to prevent it
Abstract: 

Fruit sunburn is caused by solar radiation and air temperature increase, irreversibly affecting quality, storability, and resulting in food waste. Due to global warming, fruit temperature data during heat periods are needed to create temperature distribution models to predict fruit damage. One of the aims of SHEET project is to understand the conditions inducing fruit damage, while developing a model to predict sunburn incidence based on the peak and duration of radiation and temperature degrees. By adding different orchard training systems and management practices, the generated results will contribute to strengthen the prediction model.

Innovation description: 

Real time data of fruit physiology, using plant based remote sensors, to trace fruit growth and surface temperature. The outputs give precise information on the level of stress the fruit are experiencing and allow to understand the physiological development of sunburn damages as well as the intrinsic protective mechanisms that can be activated by the fruit (i.e. through the biosynthesis of secondary metabolites). We would surely benefit from advanced visual monitoring systems to detect the temperature distribution within the canopy.

Problem addressed: 

Sunburn and heat damage appear when the fruit is subject to excess sunlight or radiant heating. Due to climate change fruit orchard are more and more subjected to this problem. The best indicator of sunburn risk is the combination of current air and fruit skin temperature. However, this relation is very variable and highly affected by other weather factors like sunlight intensity or cloud cover, humidity and wind. Factors like variety, canopy density, training system, plant water status, planting density and rootstock also affect sunburn. Moreover, position of fruit within the canopy affects greatly the risk of sunburn. Therefore, it is very difficult to predict the induction of this damage while growers are left without any tool to prevent it.

Solution offered: 

An alert system against sunburn damage that warn growers of certain temperature thresholds, over which fruit will be irreversibly damaged is the main goal of the SHEET project. This part is currently under development also by other partners, involved in geo-localizing the position of the fruit in the tree and in creating heat maps of the tree.

Innovation stage: 

In terms of research needs, sensors equipment is greatly involved in detecting punctual and precise tree organ responses to the surrounding environment. SMEs should access information and exploit it through user-friendly platforms or decision support systems (DSS). Thus, knowledge and technology transfer could be spread to end-users in hybrid meetings.

Among the various needs to allow a proper technological transfer, the technical support of advisors is crucial to allow new technologies to be known and adopted by the end users. In fact, some of these technologies (sensors and DSS) are often not user friendly enough or simply not known to the final users who will need support to apply them in the field.

SDG: 
13 – Climate Action
ICT domain: 
1.Remote Sensing
3.Proximal sensors
AGRI domain: 
1.Increase system productivity/competitiveness
9.Monitoring and controlling the production environment/system
12.Climate change mitigation and adaptation
14.Agricultural Knowledge Innovation Systems (AKIS)
17.Production quality at the farm level
FOOD domain: 
2.Reduction of food waste and losses
5.Food quality and safety
Technological Readiness Level (TRL): 
Development phase (TRL 4,5 & 6)
Project name : 
Sunburn and HEat prediction in canopies for Evolving a warning Tech solution
Project acronym: 
SHEET
Call: 
2019 – Cofund Call on ICT-enabled agri-food systems
Project duration : 
Monday, 1 February, 2021 to Wednesday, 31 January, 2024
Institution: 
Alma Mater Studiorum - University of Bologna
Role in the project : 
WP leader
Members of the research groups: 
Alexandra Boini
Gianluca Allegro
Riccardo Mazzoleni
Gianmarco Bortolotti
Luigi Manfrini
Ilaria Filippetti