Parametric model for flora detection in Middle Himalayas

Plant detection forms an integral part of the life of the forest guards, researchers, and students in the field of Botany and for common people also who are curious about knowing a plant. But detecting plants suffer a major drawback that the true identifier is only the flower and in certain species flowering occurs at major time period gaps spanning from few months to over 100 years (in certain types of bamboos). Machine Learning-based systems could be used in developing models where the experience of researchers in the field of plant sciences can be incorporated into the model. In this paper, we present a machine learning-based approach based upon other quantifiable parameters for the detection of the plant presented. The system takes plant parameters as the inputs and will detect the plant family as the output.

Amalgamation of Nanotechnology with Plant Sciences

This review paper deals with the basic aspects and advancement of Nanotechnology and its varied applications in every possible field starting from biotechnology, pharmaceuticals, drug science energy related sectors and so on. They show some explicit properties like strength, electrical, optical and chemical properties. Nanoparticles show increment in plant productivity by showing direct interactions with plants or indirectly with soil. Nanoparticles can easily be synthesized utilizing microbes and plants so they are organically protected, savvy, and climate amicable. Nanoparticles are termed as “magic bullets” because of their extraordinary properties, and for this reason they are employed in production of nano herbicides, nano pesticides. Nanotechnology does the job of addressing distinctive natural and medical problems which occurs to happen with the unreasonable utilization of pesticides and fertilizers in horticultural practices. When used in an appropriate amount and concentration they cause positive effects on the respective plants. Some of these include increment in crop production, better quality, increased photosynthetic activity, biomass, chlorophyll content and the list go on. With every positive effect comes the detrimental ones also. Inhibition of root and shoot length, reduced seed germination, inefficiency in photosynthetic activity.

Bluster or Lustre: Can AI Improve Crops and Plant Health?

In a changing climate where future food security is a growing concern, researchers are exploring new methods and technologies in the effort to meet ambitious crop yield targets. The application of Artificial Intelligence (AI) including Machine Learning (ML) methods in this area has been proposed as a potential mechanism to support this. This review explores current research in the area to convey the state-of-the-art as to how AI/ML have been used to advance research, gain insights, and generally enable progress in this area. We address the question—Can AI improve crops and plant health? We further discriminate the bluster from the lustre by identifying the key challenges that AI has been shown to address, balanced with the potential issues with its usage, and the key requisites for its success. Overall, we hope to raise awareness and, as a result, promote usage, of AI related approaches where they can have appropriate impact to improve practices in agricultural and plant sciences.

Tissue Regeneration with Hydrogel Encapsulation: A Review of Developments in Plants and Animals

Hydrogel encapsulation has been widely utilized in the study of fundamental cellular mechanisms and has been shown to provide a better representation of the complex in vivo microenvironment in natural biological conditions of mammalian cells. In this review, we provide a background into the adoption of hydrogel encapsulation methods in the study of mammalian cells, highlight some key findings that may aid with the adoption of similar methods for the study of plant cells, including the potential challenges and considerations, and discuss key findings of studies that have utilized these methods in plant sciences.

Environmental Audit Committee Call for Evidence: “Sustainability of the Built Environment”

The Centre for Natural Material Innovation in the Department of Architecture at the University of Cambridge is a cross-disciplinary centre, bringing together people and research in plant sciences, biochemistry, chemistry, fluid dynamics, engineering, and architecture. Through innovative research and experimentation, we aim to transform the way we build to achieve zero carbon emissions. Our work enables the substitution of artificial materials such as concrete and steel with nature-based materials such as timber and bamboo, and replacement of structural carbon fibre and glass fibre with hemp and flax-based biocomposites. We collaborate with other leading research institutions globally, including in the USA, China, Australia, Uruguay and others.

Bibliometric analysis of strawberry (Fragaria × ananassa Duch.) research from Plant Sciences category based on Web of Science

Strawberry (Fragaria × ananassa Duch.) is one of the most significant horticultural cash crops in the world. The study aimed to identify and analyse the 2,930 articles and review type papers of strawberry research from the Plant Sciences category based on the Web of Science. Papers mainly written in English (2,894, 98.771%), were from a total of 8,838 authors, 96 countries/territories, 1,845 organisations and published in 197 journals and book series. The top seven core journals are ranked as Plant Disease (252, 8.601%), Phytopathology (229, 7.816%), Plant Pathology (93, 3.174%), Frontiers in Plant Science (89, 3.308%), Canadian Journal of Plant Science (86, 2.935%), European Journal of Plant Pathology (86, 2.935%) and Journal of Experimental Botany (86, 2.935%), and these journals each published >86 papers. The top five countries and regions were the USA, People's Republic of China, Spain, Canada and England. The top five organisations were the University of Florida, USDA ARS, University of Malaga, University of California Davis, Agriculture and Agri-Food Canada. The top five authors are Peres, Natalia A. (Peres, Natalia; Peres, N.A.); Madden, LV; Munoz-Blanco, Juan (Munoz-Blanco, J); Schwab, Wilfried (Schwab, W) and Ellis, MA, each published >25 papers. All keywords of the strawberry research from the Plant Sciences category were separated into 11 clusters for different research topics. Visualisations offer exploratory information on the current state in a scientific field or discipline as well as indicate possible developments in the future. The review could provide a valuable guide for designing future studies.

Application of Artificial Intelligence (AI) in Plant Sciences Research

The current world needs to use modern technologies in plant science research due to increasing food requirements, and to cope up with global climate changes (1). Consequently, Artificial Intelligence (AI) based technologies are considered as the most recent and advanced research tools in every sector of science. Biological researches are also making progress surprisingly due to the assistance of AI affiliated tools directly or indirectly. Nowadays, plant physiology to genomic manipulation has witnessed, how it is easier and accurate than previous (2). Moreover, continuous monitoring of plants' growth, growing media, and relevant surroundings were time and labor-consuming in the past; even, the obtained results were erroneous somewhat. Data processing and analysis were based on individual performances of the participated researchers; surprisingly, the scenarios have been changed after the emergence of AI (3).