Introduction
We propose an alternative, reliable, autonomous, and innovative monitoring system based on new emerging technologies (Drones, Vision Sensor, IoTs Intelligent Devices with Communication Capabilities) enhanced by Algorithmic and Machine Learning techniques. Although HH is the main focus due to the large economic impact of this pest in almost all the European Countries involved in this project, the ambitious expectation of our project is to lay the foundations for autonomous field monitoring that also adapts to other insects, and eventually also other plant damaging agents. The new autonomous system should first promptly detect HH’s (or similar insects) presence and number. The same autonomous system can then be used to assess the effectiveness of the adopted countermeasures by monitoring the insect pests before and after the treatment. Moreover, since we plan to use vision sensors, we also extend the scope of the project to the storage and processing chain for discovering the internally damaged fruits undetectable to the naked eye. Thus, our project aims at guaranteeing the best food quality to the end consumer first in the fields, reducing the number of chemical treatments, and subsequently for the harvested fruits.
Background
The Brown Marmorated Stink Bug Halyomorpha halys (HH) is an invasive emerging pest of global importance for many agricultural crops and also a household nuisance due to the overwintering aggregations inside man-made structures. Due to its hitchhiking features, HH has rapidly spread since 2004 throughout the European continent where it is currently reported to have established populations in 28 countries. Both adults and nymphs feed by piercing and sucking on a great variety of fruits and seeds, rendering products unmarketable. Post-harvest damage is due to late feeding that causes internal suberifications, which in the early stage are frequently not visible to the naked eye. In 2019, the economic impact of this pest for fruit orchards (pear, apple, peach, kiwi) in Northern Italy was estimated at €588 million . Due to the high reproductive potential, very high mobility, polyphagy, and the general robustness of adult specimens, management of HH is challenging and chemical control proved to be unsatisfactory. Nonetheless, increased applications of broad spectrum-insecticides have been adopted,
disrupting previous Integrated Pest Management (IPM) programs, causing a negative impact to the environment, and undermining the final consumers’ confidence in farming.
Field monitoring is crucial to obtain information about the actual presence and abundance of insect pests in order to organize timely and proper management actions. Monitoring the insect pests mainly means estimating the number of insects to help in deciding whether to apply a countermeasure (i.e., chemical control) or not. Traditional monitoring is performed by means of active methods (visual sampling, tree beating, sweep netting). which are time, money, and energy consuming for field advisors/farmers. Alternatively, passive methods such as traps can be used. In the case of HH, commercially available traps are baited with aggregation pheromones. Thus, these traps are unreliable because they attract individuals around the trap but have a low capture rate (only 5-10% of total attracted individuals), having also the side effect of increasing damage in plants around the trap (range of 5-6 m).
Main project activities
- Reduce costs devoted to monitoring activities by growers and plant health operators: Elimination/minimization of the use of traditional monitoring devices (traps, baits) and activities (visual sampling, sweep netting, tree beating).
- Obtain reliable estimates of the number of insects in the crops: This is strategic for decision making related to management actions.
- Increase marketable fruit quality: Non-destructive techniques based on Near Infrared–Hyperspectral Imaging (NIR-HSI) coupled to Terahertz (THz), ultrasound or microwave imaging will be developed to discard fruits with defects not visible to the naked eye.
- Monitor crop health: The system built for HH monitoring becomes the foundation for systems that monitor other pests as well. In Ireland, where HH is not detected yet in the field, the system, properly adapted, could be used for detection of carroty flies (Psila rosae). In this case, drones will take pictures from traps as well as from the plants and assess the crop damage.
- Derive crop infestations models: We aim to extract knowledge to customize the countermeasures and lay the basis for an epidemiological model for HH, in spite of the bio-behavioral features of the pest that make it challenging (very high mobility and polyphagy of all developmental instars, high reproductive potential, and overlapping generations and instars).
- Logbook systems: A field-work diary, transparent to the operators. The collected information can be offered for later stages in the food-chain and potentially include further information from logistics steps up to the consumers.
Expected social impact
The proposed solutions, that are adaptable to many other insect pests (and potentially also to their natural antagonists) and could also be extended to other plant damaging agents/factors, will allow:
- A considerable reduction of work/time/energy devoted to pest monitoring activities by the field advisors/growers. The HALY.ID monitoring system will guarantee reliable pest abundance estimate without the use of traps and save a considerable amount of time and energy for the operators;
- A more efficient, targeted, and sustainable management of the pest , which will be further supported by development of specific epidemiological models. Real time monitoring given by the HALY.ID system could allow the prompt set up of preventive measures or of specific interventions exactly where/when needed, saving the crops and preventing unnecessary waste of pesticidal products. This will result in progressive restoration of IPM programs towards greater environmental sustainability. Further steps towards sustainability will be offered by the possibility to use the system for the detection and eventually the release of the natural antagonists of the target pest;
- Restoration and strengthening of consumer confidence in the agrifood industries and in the product sales chain, thanks to the increased efficiency in the selection of marketable fruits due to the HALY.ID damage detection system and the daily logbook;
- More transparency for in-field activities can be ensured with the daily logbook that can be shared with other later stages of the food-production chain.
The HALY.ID system will require an investment. However, we want to keep its cost as low as possible by using off-the-shelf components and choosing free software solutions. Besides, the system may be adopted on a territorial scale by groups/associations of farmers who could use it in turn to monitor their properties, thus saving money.
Implementation and plans to reach target groups
To the scientific community through v) Scientific Publications in peer-reviewed journals such as IEEE Journals, ACM Journals, Food Research Journals, etc, both in regular and Open Access form and vi) participation to int’l conferences, workshops, symposia. vi) Educational Programs, vii) the symposium will be open for a wide scientific audience.
To the Stakeholders: Stakeholders will be contacted on local specialized fairs and via farmer associations and cooperative companies as the Crop Production Research Center. Regular meetings with interested stakeholders, farmer associations and tester groups will be organized from year 2 where the phases of our research and hopefully a prototype will be demonstrated
Consortium
Coordinated by:
Cristina Pinotti - Università degli Studi di Perugia, Italy
Partners:
- Lara Maistrello - Università degli Studi di Modena e Reggio Emilia, Italy
- Lars Wolf - Technische Universität Braunschweig, Germany
- Dimitrios Zormpas - Tyndall National Institute, University College Cork, Ireland
- Panagiotis Sarigiannidis - University of Western Macedonia, Greece
- Dan Popescu - University POLITENICA of Bucharest, Romania
- Peter Offermans - OnePlanet Research Center/imec, the Netherlands
Funded by:
- Ministry of agricultural, food and forestry policies, Italy
- Federal Ministry of Food and Agriculture, Germany
- Department of Agriculture, Food and the Marine, Ireland
- General Secretariat for Research and Technology, Greece
- The Executive Agency for Higher Education, Research, Development and Innovation Funding, Romania
- Ministry of Agriculture, Nature and Food Quality, the Netherlands
News articles
Video
Introduction
We propose an alternative, reliable, autonomous, and innovative monitoring system based on new emerging technologies (Drones, Vision Sensor, IoTs Intelligent Devices with Communication Capabilities) enhanced by Algorithmic and Machine Learning techniques. Although HH is the main focus due to the large economic impact of this pest in almost all the European Countries involved in this project, the ambitious expectation of our project is to lay the foundations for autonomous field monitoring that also adapts to other insects, and eventually also other plant damaging agents. The new autonomous system should first promptly detect HH’s (or similar insects) presence and number. The same autonomous system can then be used to assess the effectiveness of the adopted countermeasures by monitoring the insect pests before and after the treatment. Moreover, since we plan to use vision sensors, we also extend the scope of the project to the storage and processing chain for discovering the internally damaged fruits undetectable to the naked eye. Thus, our project aims at guaranteeing the best food quality to the end consumer first in the fields, reducing the number of chemical treatments, and subsequently for the harvested fruits.
Background
disrupting previous Integrated Pest Management (IPM) programs, causing a negative impact to the environment, and undermining the final consumers’ confidence in farming.
Field monitoring is crucial to obtain information about the actual presence and abundance of insect pests in order to organize timely and proper management actions. Monitoring the insect pests mainly means estimating the number of insects to help in deciding whether to apply a countermeasure (i.e., chemical control) or not. Traditional monitoring is performed by means of active methods (visual sampling, tree beating, sweep netting). which are time, money, and energy consuming for field advisors/farmers. Alternatively, passive methods such as traps can be used. In the case of HH, commercially available traps are baited with aggregation pheromones. Thus, these traps are unreliable because they attract individuals around the trap but have a low capture rate (only 5-10% of total attracted individuals), having also the side effect of increasing damage in plants around the trap (range of 5-6 m).
Main project activities
Expected social impact
The proposed solutions, that are adaptable to many other insect pests (and potentially also to their natural antagonists) and could also be extended to other plant damaging agents/factors, will allow:
The HALY.ID system will require an investment. However, we want to keep its cost as low as possible by using off-the-shelf components and choosing free software solutions. Besides, the system may be adopted on a territorial scale by groups/associations of farmers who could use it in turn to monitor their properties, thus saving money.
Implementation and plans to reach target groups
To the scientific community through v) Scientific Publications in peer-reviewed journals such as IEEE Journals, ACM Journals, Food Research Journals, etc, both in regular and Open Access form and vi) participation to int’l conferences, workshops, symposia. vi) Educational Programs, vii) the symposium will be open for a wide scientific audience.
To the Stakeholders: Stakeholders will be contacted on local specialized fairs and via farmer associations and cooperative companies as the Crop Production Research Center. Regular meetings with interested stakeholders, farmer associations and tester groups will be organized from year 2 where the phases of our research and hopefully a prototype will be demonstrated
Consortium
Cristina Pinotti - Università degli Studi di Perugia, Italy
Partners:
Funded by:
News articles
Novel Smart Trap with Edge AI for Pest Insect Monitoring
Near infrared image sensors for monitoring insect pests in crop fields
Video