The main objective of the ECOINSECT Operational Group project is to develop functional protein flours and sustainable fertilizers/ fertilizers through the use of vegetable by-products from conventional and organic crops as a substrate for the growth of Tenebrio molitor, thus promoting circular bioeconomy.

To achieve this goal, the suitability of horticultural by-products generated in the Costa Tropical of Andalusia as food for T. molitor will be evaluated, selecting those that improve its growth. Once the flours are obtained, their nutritional quality, digestibility, functional potential, and stability will be evaluated, considering also the efficiency and energy expenditure of the process.

On the other hand, the nutritional profile and bio-stimulant and bio-pesticidal activity of the guano (formed by insect excreta and other remains of their feeding and growth) will be characterized in order to valorize it and use it as a sustainable fertilizer. Once obtained, it will be applied in organic crops and improvements in their development and the organoleptic quality of the fruits obtained will be studied.

Finally, ICT tools involving the use of Artificial Intelligence, Big Data, and Machine Learning will be employed to, with all the information generated, develop an algorithm that allows predicting the nutritional and functional profile of the flours and fertilizers based on the characteristics of the substrates used to feed the T. molitor larvae, as well as the efficiency and improvement of organic crops after the application of the generated fertilizers.

The innovative nature of the project lies in the circular bioeconomy system itself that is intended to be created within the agri-food sector: starting from different plant waste from conventional and organic cultivation to use them as substrates on which the Tenebrio molitor insect can grow, developing new functional protein flours (both for animal and human consumption) and new sustainable fertilizers from the excreta that can be used in organic crops. Therefore, it is promoted that the main byproducts generated in the value chain are revalued within the same system, making it a closed circular system, which significantly reduces the carbon footprint of the process in addition to the environmental and economic benefits subsequent to the use and valorization of the waste generated during the system.


ECOINSECT Project: Transforming Waste into Sustainable Resources

  • Utilization of Horticultural Waste:

We investigate the use of cucumber and cherry tomato waste, both from conventional and organic cultivation, to enrich the diet of Tenebrio molitor larvae. This approach not only offers a valuable alternative to plant waste but also addresses the management problem for horticultural companies.

  • Energy Efficiency and Waste Reduction:

Supplementing the diet of T. molitor with these waste materials results in energy and raw material savings for insect farms. Larval growth efficiency is doubled, reducing breeding time by 3-4 weeks.

  • Processing of Insect Flours:

The method is scalable to obtain insect flours and preserves bioactive compounds and healthy fatty acids. Although there are slight variations in stability and color, the resulting flour remains microbiologically stable for at least three months.

  • Nutritional Improvement and Sustainability:

Supplementation with horticultural waste improves the nutritional profile of the larvae and flours, reducing fat content and increasing proteins, polyunsaturated fatty acids, vitamins, and minerals. These flours are ingredients with higher nutritional value and healthy potential.

  • Agricultural Benefits of Guanos:

The guanos from supplemented larvae benefit vegetative growth and fruit production in tomato plants. Currently, a Field Study is being conducted in organic Dutch cucumber cultivation.

  • Technological Innovation:

Prototypes of predictive models have been developed using Artificial Intelligence, Big Data, and Machine Learning to predict efficiencies in obtaining processes and the nutritional profile of the bioproducts (flours or fertilizers).

  • Traceability and Blockchain:

We evaluate the feasibility of implementing a Blockchain system for traceability control, with "Smart contracts" designed for each phase of the production and commercialization of T. molitor products.
In the second year, we will continue to develop prototypes to predict the nutritional and functional profile of the products, as well as the efficiency and improvement of the characteristics of organic crops through the use of guanos as biofertilizers.

Here are some links where some results obtained so far are explained:

[Video 1] and [Video 2]

Project Data:
  • Funding Entity: Subsidized by the European Agricultural Fund for Rural Development (FEADER), and by the Andalusian Government through the Ministry of Agriculture, Livestock, Fisheries, and Sustainable Development.
  • Announcement: Grants aimed at supporting the operation of Operational Groups of the European Innovation Partnership (EIP) in the field of agricultural productivity and sustainability for the implementation of pilot projects and the development of new products, practices, processes, and technologies in the agricultural, food, and forestry sectors, within the framework of the Rural Development Program of Andalusia 2014-2020.
  • Modality: Line 1. Operation 16.1.2: Aid for the operation of Operational Groups of the European Innovation Partnership (EIP) in the field of agricultural productivity and sustainability.
  • Consortium Partners: CIDAF, LA CAÑA, ECONATUR, and Agri-Food Cooperatives of Andalusia-Granada
  • Contributors: FIDESOL e Insectalia
  • Funding: Junta de Andalucía and the European Agricultural Fund for Rural Development (FEADER). €236,092.00
  • Deadline Execution: 2022/2024


  • Bioconversion of underutilized plant byproducts through the use of insects with the aim of valorizing them.
  • Optimization of drying methodologies for obtaining insect flours with high nutritional quality and functional potential.
  • ICT tools involving the use of Artificial Intelligence, Big Data, and Machine Learning for the development of a predictive model that can be applied to optimize the feeding of mealworms based on the desired nutritional and functional characteristics (flour) and bio-stimulants (fertilizer) to be obtained.