Paul is one of the #DigitAg co-funded PhDs
Quantification of pest regulation by generalist predators by analysis of image sequence to determine and quantify network of interactions, case of the banana weevil
- Start Date: October 2018
- University: MUSE Montpellier University of Excellence / Montpellier SupAgro
- PhD School: GAIA
- Field(s): Bioinformatics, Agroecology
- Doctoral Thesis Advisor(s): Philippe Tixier, Cirad GECO & William Puech, Université de Montpellier LIRMM
- Co-supervisors: Dominique Carval, Cirad GECO & Wiliam Puech, Université de Montpellier LIRMM
- Funding: #DigitAg – Cirad
- #DigitAg: Axes 3 et 5 – Challenges 1, 3 et 8
Keywords: Cosmopolites, biological control, Convolutional Neural Network, Crop protection, pest management, biocontrol, banana
Abstract:
In the context of sustainable development in agriculture, it is crucial to define integrated pest management methods. Among these, pest regulation by natural enemies represents a promising way. However, to date, there is no method (i) to identify with certitude the generalist predators that are involved in the biocontrol of pests and (ii) to quantify the effect of this biocontrol on the population dynamics of the pests (i.e. the effectiveness of biocontrol in the field). These knowledges are crucial for the development and the transfer of effective agroecological management practices.
We propose the use of in natura mesocosms coupled with a non-perturbing video measurement method of interaction networks to quantify regulation and predation. These mesocosms will correspond to existing banana production situations in Martinique, the study area of the GECO unit, and will cover a large range of plant biodiversity (planned or not). In each of these mesocosms, the population dynamics of the banana weevil Cosmopolites sordidus will be followed by capture-mark-recapture method in order to determine the magnitude of the regulation by the natural enemies that occurs. In each selected situation, we will establish a control corresponding to a plot where natural enemies are excluded. The video method will allow to know the identity of the predators involved in the regulations and to quantify the links of trophic and non-trophic interactions existing in the animal community (frequency, duration and type of interactions). To this end, methods of automated digital image analysis will be developed (e.g. automatic recognition of a pest, count of individuals), including machine learning and in particular convolutional neural network methods that are developed by the ICAR team (LIRMM unit). These methods should revolutionize our understanding of the functioning of agrosystems and, because of their genericity, can be applied to the study of most cultivated systems.
Papers/Communications
Tresson P., Tixier P., Puech W., Bagny Beile L., Roudine S., Pagès C., Carval D. (2019) CORIGAN Assessing multiple species and interactions within images, Methods in Ecology & Evolution – https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13281
Tresson P., Tixier P., Puech W., Carval D., (2019) Insect interaction analysis based on object detection and CNN, IEEE MMSP – https://ieeexplore.ieee.org/abstract/document/8901798
Contact: paul.tresson [AT] cirad.fr ou [AT] lirmm.fr – +33 4 67 41 85 85
