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

作者：Stan Chabert, Jordan Bernigaud-Samatan, Benjamin K. Blackman, Nicolas Blanchet, Olivier Catrice, Cécile Donnadieu, Marianne Gani, Rémi Grousset, Salena Husband, Guillaume Tueux, Silvio Erler, Nicolas Langlade · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2026 · DOI：10.64898/2026.07.08.737348 · 研究领域：Plant and animal studies、Smart Agriculture and AI、Remote Sensing in Agriculture

Abstract 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 the three insect classes visiting the most sunflower (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. Poll...