Philippe Morel

Bio: Philippe Morel is an academic researcher from University of Angers. The author has contributed to research in topics: Hydrangea macrophylla & Hydrangea aspera. The author has an hindex of 11, co-authored 26 publications receiving 857 citations. Previous affiliations of Philippe Morel include Institut national de la recherche agronomique.

Bud structure, position and fate generate various branching patterns along shoots of closely related Rosaceae species: a review

Abstract: Branching in temperate plants is closely linked to bud fates, either floral or vegetative. Here, we review how the fate of meristematic tissues contained in buds and their position along a shoot imprint specific branching patterns which differ among species. Through examples chosen in closely related species in different genera of the Rosaceae family, a panorama of patterns is apparent. Patterns depend on whether vegetative and floral buds are borne individually or together in mixed buds, develop as the shoot grows or after a rest period, and are located in axillary or terminal positions along the parent shoot. The resulting branching patterns are conserved among varieties in a given species but progressively change with the parent shoot length during plant ontogeny. They can also be modulated by agronomic and environmental conditions. The existence of various organizations in the topology and fate of meristematic tissues and their appendages in closely related species questions the between-species conservation of physiological and molecular mechanisms leading to bud outgrowth vs. quiescence and to floral induction vs. vegetative development.

Using Deep Learning for Image-Based Plant Disease Detection

Abstract: Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.

A review of imaging techniques for plant phenotyping.

Abstract: Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review.