Bernigaud-Samatan, J. | Search bioRxiv

bioRxiv2026

PolliCrop: A high-throughput computer vision pipeline for pollinator monitoring in agroecosystems

Flower-visiting insect populations are declining since the 1990s, especially because of the decrease of floral resources in agricultural settings. Mass flowering crops can help increase resource availability, and plant breeding can be directed towards selecting varieties attracting more flower-visiting insects. This requires the implementation of an automated high-throughput phenotyping tool for assessing the attractiveness of plant genotypes to flower-visiting insects. In this study, (i) we present a procedure to take standardized images of sunflower heads with camera traps continuously at day and night in the field; (ii) we trained two versions of a deep learning model, named PolliCrop, to automatically detect and identify three classes of the main insects visiting sunflower on these images (non-Bombus bees, bumble bees, lepidopterans); (iii) we assessed and validated the ability of PolliCrop to correctly predict the true visitation frequencies of the insect classes on three sunflower genotypes; (iv) we presented two statistical approaches to compare the insect visitation frequencies between plant genotypes, one including weather variables, and the other one without. One PolliCrop version yielded satisfying performance to correctly detect the three insect classes. In particular, it correctly predicted the insect visitation frequencies on two sunflower genotypes in a range of {+/-}10%. The other PolliCrop version can be useful in certain contexts of images and objectives. PolliCrop can be extended in the future to other crop species by training PolliCrop on new images captured in these crops. The field experimental design to set up for comparing the attractiveness between genotypes is also discussed.

animal behavior and cognition↗

bioRxiv2026

A plant single nucleotide polymorphism impacts nectar sugar composition, microbial diversity and pollinator visits

Nectar is a hub for plant-pollinator interactions, yet gene-level causal links between plant genetic variation, pollinator foraging and nectar microbial assembly remain poorly resolved. Using near-isogenic lines, innovative field time-lapse monitoring of pollinator visits and long-read amplicon sequencing of nectar microbiota, we show that a natural single-nucleotide variant at a cell-wall invertase gene (HaCWINV2) controls sunflower nectar chemistry and influences both pollinators and microbes. Plants homozygous for a loss-of-function HaCWINV2 allele produce sucrose-rich nectar, resulting in fewer bee visits under field conditions, while bumblebee visitation remained unaffected. In pollinator-excluded flowers, invertase-deficient plants harboured greater fungal diversity and compositionally distinct communities, indicating that nectar sugar profiles act as ecological filters shaping the nectar microbiome. This loss-of-function allele was found rarely and only at the heterozygote state in wild sunflowers and was fixed in 35% of cultivated lines indicating a positive selection during domestication. Our findings establish a causal link between a single gene and nectar chemistry with cascading ecological effects in a plant-pollinator system, illustrating how subtle genetic changes scale up to alter nectar traits, microbial assembly and pollinator foraging behaviour.

genetics↗