Agriculture and farming are the most important and basic industries that are very important to humanity and generate a considerable portion of any nation's GDP. For good agricultural and farming management, technological advancements and support are required. Smart agriculture (or) farming is a set of approaches that uses a variety of current information and communication technology to improve the production and quality of agricultural products with minimum human involvement and at a lower cost. Smart farming is mostly based on IoT technology, since there is a need to continually monitor numerous aspects in the agricultural field, such as water level, light, soil characteristics, plant development, and so on. Machine learning algorithms are used in smart farming to increase production and reduce the risk of crop damage. Data analytics has been shown through extensive study to improve the accuracy and predictability of smart agricultural systems. Data analytics is utilised in agricultural fields to make decisions and recommend acceptable crops for production. This study provides a comprehensive overview of the different methods and structures utilised in smart farming. It also provides a thorough analysis of different designs and recommends appropriate answers to today's smart farming problems.

SummaryOne of the infrastructure‐free networks is mobile ad hoc networks (MANETs) that are built with limited battery life using wireless mobile devices. This restricted battery capability in MANETs creates the necessity of considering the energy‐awareness constraint in designing them. As routing protocols, the major aim of MANETs is to create the energy awareness in the network; it improves the network's lifetime through effectively utilizing the available restricted energy. Moreover, it creates some limitations like the mobility constraint, wireless link's sensitivity to environmental impacts, and restricted transmission range and residual energy of nodes that causes rapid modifications in the network topology and frequent link failure. By taking those problems, this paper plans to develop a new multipath routing protocol, where the hybrid optimization algorithm with the integration of cuckoo search optimization (CSO) and butterfly optimization algorithm (BOA) is proposed and named sensory modality‐based cuckoo search butterfly optimization (SM‐CSBO) for determining the optimal path between the source and destination. The main goal is to select the path with better link quality and more stable links to guarantee reliable data transmission. The multi‐objective function is considered with the factors regarding distance, normalized energy, packet delivery ratio, and control overhead to develop an effective routing protocol in MANET. The proposed model of SM‐CSBO algorithm has superior than 5.8%, 30.4%, 36.7%, and 39.3%, correspondingly maximized than PSO, SFO, CSO, and SFO algorithms while considering the number of nodes as 150. The simulation outcomes proved that it enhances network performance when compared with the other traditional protocols.

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