Segmentación de instancias para detección automática de malezas y cultivos en campos de cultivo

Con base en las recientes aplicaciones exitosas de técnicas de Aprendizaje Profundo en la clasificación, detección y segmentación de plantas, proponemos un enfoque de segmentación de instancias utilizando un modelo Mask R-CNN para la detección de malezas y cultivos en tierras de cultivo. Evaluamos el rendimiento de nuestro modelo con la métrica de precisión promedio de MSCOCO, contrastando el uso de técnicas de aumento de datos. Los resultados obtenidos muestran cómo el modelo se adapta muy bien en este contexto, abriendo nuevas oportunidades para soluciones automatizadas de control de malezas a gran escala

Machine learning using digitized herbarium specimens to advance phenological research

Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth

Overview of LifeCLEF plant identification task 2019: Diving into data deficient tropical countries

Automated identification of plants has improved considerably thanks to the recent progress in deep learning and the availability of training data. However, this profusion of data only concerns a few tens of thousands of species, while the planet has nearly 369K. The LifeCLEF 2019 Plant Identification challenge (or "PlantCLEF 2019") was designed to evaluate automated identification on the flora of data deficient regions. It is based on a dataset of 10K species mainly focused on the Guiana shield and the Northern Amazon rainforest, an area known to have one of the greatest diversity of plants and animals in the world. As in the previous edition, a comparison of the performance of the systems evaluated with the best tropical flora experts was carried out. This paper presents the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes

LifeCLEF plant identification task 2014

The LifeCLEFs plant identification task provides a testbed for a system-oriented evaluation of plant identification about 500 species trees and herbaceous plants. Seven types of image content are considered: scan and scan-like pictures of leaf, and 6 kinds of detailed views with unconstrained conditions, directly photographed on the plant: flower, fruit, stem & bark, branch, leaf and entire view. The main originality of this data is that it was specifically built through a citizen sciences initiative conducted by Tela Botanica, a French social network of amateur and expert botanists. This makes the task closer to the conditions of a real-world application. This overview presents more precisely the resources and assessments of task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results. With a total of ten groups from six countries and with a total of twenty seven submitted runs, involving distinct and original methods, this fourth year task confirms Image & Multimedia Retrieval community interest for biodiversity and botany, and highlights further challenging studies in plant identification. (Résumé d'auteur

Contribution citoyenne au suivi de la flore d'un parc national français, un exemple remarquable à l'échelle du Parc national des Cévennes

International audienceThe diversity of habitats in the Cévennes national Park is home to a rich flora, comprising more than 2400 species (angiosperms, gymnosperms, and ferns). A good knowledge of this flora is essential for the development of adapted management strategies. However, human resources for this purpose are limited, and the support of residents and visitors can help to increase the capacity to identify this biodiversity. As the rapid and correct identification of a large number of plant species by non-botanists is difficult, the Cévennes national Park has invested in the development of a new method based on automatic visual approaches to increase citizen participation. We report this experience here, based on the citizen scientific platform Pl@ntNet. This study underlines both the interest of civil society in this approach, the results it brings, and lays the foundations to facilitate its deployment in other French reserves and parksLa diversité des habitats du Parc national des Cévennes héberge une flore riche, composée de plus de 2400 espèces (angiospermes, gymnospermes et fougères). Une bonne connaissance de cette flore est essentielle pour le développement de stratégies de gestion adaptées. Les ressources humaines étant cependant limitées, l’appui des résidents et visiteurs, peut contribuer à multiplier les capacités de recensement de cette biodiversité. L’identification rapide et correcte d’un grand nombre d’espèces végétales étant difficile par des non-spécialistes de cette flore, le Parc national des Cévennes s’est investi dans le développement d’une nouvelle méthode basée sur des approches visuelles automatiques pour accroître la participation des citoyens. Nous rapportons ici cette expérience, s’appuyant sur la plateforme scientifique citoyenne Pl@ntNet. Cette étude souligne à la fois l’intérêt de la société civile pour cette approche, évalue les résultats qu’elle apporte, et pose les bases pour faciliter son déploiement dans d’autres réserves naturelles et parcs français