The growing world population is calling for a new wave of innovations in agriculture, with the objective to produce more food in a sustainable way.
Thus, farmers all across the world are under increasing pressure to feed the growing world population, which will need 50% more food to be produced within 2030.
The increasing demand of fresh products also reflects into the growing adoption of greenhouse farming, which allows to extend the production of fruits and vegetables throughout the year.
However, to keep up with the demand, farming (and greenhouse farming) must increase the level of automation in all field operations, allowing effective and continuous planting and harvesting of vegetables.
Robotics is seen as a key resource to streamline farming practices, but issues as speed, accuracy, autonomy and cost are keeping most solutions still at the demonstrator stage.
Although several ongoing attempts are being made to automate greenhouse farming all over the world, none of them is being successfully applied to harvesting of soft vegetables, due to the intrinsic issues of automatically recognizing ripe fruits (artificial vision) and picking them with the necessary repetition rate and accuracy.
The main problems are the capacity to discriminate ripe from unripe fruits through fast and accurate machine vision systems and then to pick soft and pliable fruits without damaging them.
Currently, no solution on the market exists that is able to reach such performances, which in turn would open the gates for a 1B€ market by 2020
MetoMotion, an Israeli start-up, has developed a multi-purpose robotic system for labor demanding tasks in greenhouses, called GRoW (Greenhouse Robotic Worker).
GRoW is a technology platform open to host solutions for pruning, pollinating, de-leafing and harvesting of different crops, like eggplants, cucumbers, peppers, tomatoes, just to name but a few.
For the first time in robotics for agriculture, GRoW delivers a fully autonomous solution that can be easily integrated into current greenhouse practices, from the identification of the ripe fruit up to picking and packing it into the box.
By doing so, it also generates and processes a continuous data flow relating to the plant growth and plant stress, thus assisting the grower through decision making and planning.
Thanks to its proprietary advanced vision system and computational algorithms, flexible picking system and autonomous driving, GRoW is poised to fully meet the market requirements, proving to be accurate (error rate < 5 %), fast (harvesting rate > 720 kg/hour), easy to use and cost-effective, with an expected payback time for customers point of about 3 years. Furthermore, GRoW is a flexible technological platform adaptable to a variety of crops and agricultural operations.
As first application the harvesting of tomato in high-tech greenhouses has been chosen because most grown crop worldwide (35% of all vegetables).
The objectives of the feasibility study was linked with:
1. The understanding of specific technological criteria to be achieved by GRoW in order to be compliant with the growers' requirements.
2. The definition of the most suitable commercialisation strategy and entry points.
