Plant pathogens infecting marijuana (Cannabis sativa L.) plants reduce growth of the crop by affecting the roots, crown, and foliage. In addition, fungi (molds) that colonize the inflorescences (buds) during development or after harvest, and which colonize internal tissues as endophytes, can reduce product quality. The pathogens and molds that affect C. sativa grown hydroponically indoors (in environmentally controlled growth rooms and greenhouses) and field-grown plants were studied over multiple years of sampling. A PCR-based assay using primers for the internal transcribed spacer region (ITS) of ribosomal DNA confirmed identity of the cultures. Root-infecting pathogens included Fusarium oxysporum, Fusarium solani, Fusarium brachygibbosum, Pythium dissotocum, Pythium myriotylum, and Pythium aphanidermatum, which caused root browning, discoloration of the crown and pith tissues, stunting and yellowing of plants, and in some instances, plant death. On the foliage, powdery mildew, caused by Golovinomyces cichoracearum, was the major pathogen observed. On inflorescences, Penicillium bud rot (caused by Penicillium olsonii and Penicillium copticola), Botrytis bud rot (Botrytis cinerea), and Fusarium bud rot (F. solani, F. oxysporum) were present to varying extents. Endophytic fungi present in crown, stem, and petiole tissues included soil-colonizing and cellulolytic fungi, such as species of Chaetomium, Trametes, Trichoderma, Penicillium, and Fusarium. Analysis of air samples in indoor growing environments revealed that species of Penicillium, Cladosporium, Aspergillus, Fusarium, Beauveria, and Trichoderma were present. The latter two species were the result of the application of biocontrol products for control of insects and diseases, respectively. Fungal communities present in unpasteurized coconut (coco) fiber growing medium are potential sources of mold contamination on cannabis plants. Swabs taken from greenhouse-grown and indoor buds pre- and post-harvest revealed the presence of Cladosporium and up to five species of Penicillium, as well as low levels of Alternaria species. Mechanical trimming of buds caused an increase in the frequency of Penicillium species, presumably by providing entry points through wounds or spreading endophytes from pith tissues. Aerial distribution of pathogen inoculum and mold spores and dissemination through vegetative propagation are important methods of spread, and entry through wound sites on roots, stems, and bud tissues facilitates pathogen establishment on cannabis plants.
Background Glandular capitate trichomes which form on bract tissues of female inflorescences of high THC-containing Cannabis sativa L. plants are important sources of terpenes and cannabinoids. The influence of plant age and cannabis genotype on capitate trichome development, morphology, and maturation has not been extensively studied. Knowledge of the various developmental changes that occur in trichomes over time and the influence of genotype and plant age on distribution, numbers, and morphological features should lead to a better understanding of cannabis quality and consistency. Methods Bract tissues of two genotypes—“Moby Dick” and “Space Queen”—were examined from 3 weeks to 8 weeks of flower development using light and scanning electron microscopy. Numbers of capitate trichomes on upper and lower bract surfaces were recorded at different positions within the inflorescence. Observations on distribution, extent of stalk formation, glandular head diameter, production of resin, and extent of dehiscence and senescence were made at various time points. The effects of post-harvesting handling and drying on trichome morphology were examined in an additional five genotypes. Results Two glandular trichome types—bulbous and capitate (sessile or stalked)—were observed. Capitate trichome numbers and stalk length were significantly (P = 0.05) greater in “Space Queen” compared to “Moby Dick” at 3 and 6 weeks of flower development. Significantly more stalked-capitate trichomes were present on lower compared to upper bract surfaces at 6 weeks in both genotypes, while sessile-capitate trichomes predominated at 3 weeks. Epidermal and hypodermal cells elongated to different extents during stalk formation, producing significant variation in length (from 20 to 1100 μm). Glandular heads ranged from 40 to 110 μm in diameter. Maturation of stalked-capitate glandular heads was accompanied by a brown color development, reduced UV autofluorescence, and head senescence and dehiscence. Secreted resinous material from glandular heads appeared as droplets on the cuticular surface that caused many heads to stick together or collapse. Trichome morphology was affected by the drying process. Conclusion Capitate trichome numbers, development, and degree of maturation were influenced by cannabis genotype and plant age. The observations of trichome development indicate that asynchronous formation leads to different stages of trichome maturity on bracts. Trichome stalk lengths also varied between the two genotypes selected for study as well as over time. The variability in developmental stage and maturation between genotypes can potentially lead to variation in total cannabinoid levels in final product. Post-harvest handling and drying were shown to affect trichome morphology.
Over the last decade, the number of digital images captured per day witnessed a massive explosion. Nevertheless, the visual quality of photographs is often degraded by noise during image acquisition or transmission. With the re-emergence of deep neural networks, the performance of image denoising techniques has been substantially improved in recent years. The objective of this paper is to provide a comprehensive survey of recent advances in image denoising techniques based on deep neural networks. In doing so, we commence with a thorough description of the fundamental preliminaries of the image denoising problem followed by highlighting the benchmark datasets and the widely used metrics for objective assessments. Subsequently, we study the existing deep denoisers in the supervised and unsupervised categories and review the technical specifics of some representative methods within each category. Last but not least, we conclude the analysis by remarking on trends and challenges in the development of better state-of-the-art algorithms and future research.
Over the last decade, the number of digital images captured per day witnessed a massive explosion. Nevertheless, the visual quality of photographs is often degraded by noise during image acquisition or transmission. With the re-emergence of deep neural networks, the performance of image denoising techniques has been substantially improved in recent years. The objective of this paper is to provide a comprehensive survey of recent advances in image denoising techniques based on deep neural networks. In doing so, we commence with a thorough description of the fundamental preliminaries of the image denoising problem followed by highlighting the benchmark datasets and the widely used metrics for objective assessments. Subsequently, we study the existing deep denoisers in the supervised and unsupervised categories and review the technical specifics of some representative methods within each category. Last but not least, we conclude the analysis by remarking on trends and challenges in the development of better state-of-the-art algorithms and future research.
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