Plants in vitro propagation with its applications in food, pharmaceuticals and cosmetic industries; current scenario and future approaches

Plant tissue culture technique employed for the identification and isolation of bioactive phytocompounds has numerous industrial applications. It provides potential benefits for different industries which include food, pharmaceutical and cosmetics. Various agronomic crops i.e., cereals, fruits, vegetables, ornamental plants and forest trees are currently being used for in vitro propagation. Plant tissue culture coupled with biotechnological approaches leads towards sustainable agricultural development providing solutions to major food security issues. Plants are the rich source of phytochemicals with medicinal properties rendering them useful for the industrial production of pharmaceuticals and nutraceuticals. Furthermore, there are numerous plant compounds with application in the cosmetics industry. In addition to having moisturizing, anti‐ageing, anti‐wrinkle effects; plant-derived compounds also possess pharmacological properties such as antiviral, antimicrobial, antifungal, anticancer, antioxidant, anti-inflammatory, and anti-allergy characteristics. The in vitro propagation of industrially significant flora is gaining attention because of its several advantages over conventional plant propagation methods. One of the major advantages of this technique is the quick availability of food throughout the year, irrespective of the growing season, thus opening new opportunities to the producers and farmers. The sterile or endangered flora can also be conserved by plant micro propagation methods. Hence, plant tissue culture is an extremely efficient and cost-effective technique for biosynthetic studies and bio-production, biotransformation, or bioconversion of plant-derived compounds. However, there are certain limitations of in-vitro plant regeneration system including difficulties with continuous operation, product removal, and aseptic conditions. For sustainable industrial applications of in-vitro regenerated plants on a large scale, these constraints need to be addressed in future studies

A new deep learning-based technique for rice pest detection using remote sensing

Background Agriculture plays a vital role in the country’s economy and human society. Rice production is mainly focused on financial improvements as it is demanding worldwide. Protecting the rice field from pests during seedling and after production is becoming a challenging research problem. Identifying the pest at the right time is crucial so that the measures to prevent rice crops from pests can be taken by considering its stage. In this article, a new deep learning-based pest detection model is proposed. The proposed system can detect two types of rice pests (stem borer and Hispa) using an unmanned aerial vehicle (UAV). Methodology The image is captured in real time by a camera mounted on the UAV and then processed by filtering, labeling, and segmentation-based technique of color thresholding to convert the image into greyscale for extracting the region of interest. This article provides a rice pests dataset and a comparative analysis of existing pre-trained models. The proposed approach YO-CNN recommended in this study considers the results of the previous model because a smaller network was regarded to be better than a bigger one. Using additional layers has the advantage of preventing memorization, and it provides more precise results than existing techniques. Results The main contribution of the research is implementing a new modified deep learning model named Yolo-convolution neural network (YO-CNN) to obtain a precise output of up to 0.980 accuracies. It can be used to reduce rice wastage during production by monitoring the pests regularly. This technique can be used further for target spraying that saves applicators (fertilizer water and pesticide) and reduces the adverse effect of improper use of applicators on the environment and human beings

Rice Crop Counting Using Aerial Imagery and GIS for the Assessment of Soil Health to Increase Crop Yield

first_pagesettingsOrder Article Reprints Open AccessArticle Rice Crop Counting Using Aerial Imagery and GIS for the Assessment of Soil Health to Increase Crop Yield by Syeda Iqra Hassan 1,2,3ORCID,Muhammad Mansoor Alam 4,5,6,7ORCID,Muhammad Yousuf Irfan Zia 3ORCID,Muhammad Rashid 8ORCID,Usman Illahi 9ORCID andMazliham Mohd Su’ud 5,10,* 1 Department of Electronics and Electrical Engineering, Universiti Kuala Lumpur British Malaysian Institute (UniKL BMI), Batu 8, Jalan Sungai Pusu, Gombak 53100, Malaysia 2 National Centre for Big Data and Cloud Computing, Ziauddin University, Karachi 74600, Pakistan 3 Department of Electrical Engineering, Ziauddin University, Karachi 74600, Pakistan 4 Faculty of Computing, Riphah International University, Islamabad 46000, Pakistan 5 Faculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, Malaysia 6 Malaysian Institute of Information Technology, University of Kuala Lumpur, Kuala Lumpur 50250, Malaysia 7 Faculty of Engineering and Information Technology, School of Computer Science, University of Technology, Sydney 2006, Australia 8 Department of Computer Engineering, Umm Al Qura University, Makkah 21955, Saudi Arabia 9 Department of Electrical Engineering, FET, Gomal University, Dera Ismail Khan 29050, Pakistan 10 Water and Engineering Section, MFI, Universiti Kuala Lumpur Malaysian France Institute (UniKL MFI), Section 14, Jalan Damai, Seksyen 14, Bandar Baru Bangi 43650, Malaysia * Author to whom correspondence should be addressed. Sensors 2022, 22(21), 8567; https://doi.org/10.3390/s22218567 Received: 16 September 2022 / Revised: 23 October 2022 / Accepted: 1 November 2022 / Published: 7 November 2022 (This article belongs to the Section Smart Agriculture) Download Browse Figures Versions Notes Abstract Rice is one of the vital foods consumed in most countries throughout the world. To estimate the yield, crop counting is used to indicate improper growth, identification of loam land, and control of weeds. It is becoming necessary to grow crops healthy, precisely, and proficiently as the demand increases for food supplies. Traditional counting methods have numerous disadvantages, such as long delay times and high sensitivity, and they are easily disturbed by noise. In this research, the detection and counting of rice plants using an unmanned aerial vehicle (UAV) and aerial images with a geographic information system (GIS) are used. The technique is implemented in the area of forty acres of rice crop in Tando Adam, Sindh, Pakistan. To validate the performance of the proposed system, the obtained results are compared with the standard plant count techniques as well as approved by the agronomist after testing soil and monitoring the rice crop count in each acre of land of rice crops. From the results, it is found that the proposed system is precise and detects rice crops accurately, differentiates from other objects, and estimates the soil health based on plant counting data; however, in the case of clusters, the counting is performed in semi-automated mode

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality