In the present scenario, the term wireless body area network (WBAN) is becoming an integral part of human day to day life due its wide variety of applications, especially in the field of healthcare systems. To design such a reliable body area network system, there are a number of factors to be considered both in hardware and software levels. One of such factors still developing is the design and the analysis of routing protocols in the network layer. Routing protocols are a set of protocols which can identify and maintain the routes in the network so that the data can be exchanged between the nodes efficiently. Hence, routing protocol plays a vital role in the wireless sensor networks for reliable communication between the sensor nodes. In this paper, different routing protocols for body area networks are surveyed and observed that they are affected by factors like energy, network topology, various quality of services (QoS) in the nodes, node temperature, transmission range of nodes, human posture, etc. An evocative taxonomy of protocols is presented such as cluster-based, cross-layered, postural movement based, QoS aware and temperature-aware routing protocols. From the survey, it is found that the selection of a routing protocol is application dependent. For example, the energy efficient protocols like reinforcement learning based routing with QoS support or wireless autonomous spanning tree protocol can be used for daily health monitoring systems due to its high packet delivery ratio. If the system is for in vivo networks, routing algorithm for network of homogeneous and Id-less biomedical sensor nodes or mobility-supporting adaptive threshold-based Thermal-aware energy-efficient multi-hop protocols are the suitable ones. For critical and emergency cases, where accuracy with little delay is the major concern, the protocols like critical data routing, reliability aware routing, data-centric multi objective QoS-aware routing protocol, etc. can be rightly chosen. This entire survey paper can be used by the researchers as a reference for studying various WBAN routing protocols, especially in the field of medical health care systems.

No abstract

No abstract

Allelopathy is defined as both beneficial and deleterious biochemical interaction between plant and weeds, and / or plants and microorganisms through the production of chemical compounds that escape into the environment and subsequently influence the growth and development of neighboring plants. Allelochemicals are present in all types of tissues and are released into the rhizosphere by a variety of mechanism, including decomposition of residues, volatilization and root exudation. Allelochemical its structure and mode of action are different and may offer a potential for future development of herbicide. This paper describes the variety of weeds and crop species that exihibit aallelopathic interface either with crop or weeds.

No abstract

No abstract

No abstract

Computer Vision is one of the most fascinating and challenging tasks in the field of Artificial Intelligence. Computer Vision serves as a link between computer software and the visuals we see around us. It enables computer software to comprehend and learn about the visuals in its environment. As an example: The fruit is determined by its color, shape, and size. This job may seem simple for the human brain, but in the Computer Vision pipeline, we first collect data, then conduct data processing operations, and then train and educate the model to learn how to differentiate between fruits based on size, shape, and color. The main goal is to identify and comprehend the images and offer new images that are more useful for us in different life fieldsThe term "OpenCV" is an abbreviation for "open source computer vision." The architecture is made up of software, databases, and plugins that are pre-programmed with support for integrating computer vision applications [3]. It is one of the most used toolkits with a large developer group. It is well-known for the size at which it builds realworld usage cases for industrial use. OpenCV follows C/C++, Python, Java programming languages and can be used to build computer vision software for desktop and smartphone platforms such as Windows, Linux, macOS, Android, and iOS. The most recent releases are OpenCV-4.5.2 and OpenCV-3.4.14. It is free and open-source, as well as simple to use and install. It is intended for numerical productivity with a heavy emphasis on real-time applications. The first version was in the C programming language; however, its success increased with the release of Version 2.0, which had a C++ implementation [2]. C++ is used to create new features. OpenCV can be downloaded for free from http://opencv.org. This platform includes the most recent distribution update (version: 4.5.2) as well as older iterations. Photos must be in BGR or Grayscale format in order to be displayed or saved via OpenCV. Otherwise, unfavorable outcomes could occur [1].Face detection is a form of computer vision that aids in detecting and visualizing facial features in captured pictures or real-time videos. This type of object detection technique detects instances of semantic artifacts of a given class (such as people, cars, and houses) in digital pictures and videos. Face recognition has become increasingly important as technology has advanced, especially in fields such as photography, defense, and marketing [4], [5].