Smart Sensing with Edge Computing in Precision Agriculture for Soil Assessment and Heavy Metal Monitoring: A Review

Abstract

With the implementation of the Internet of Things, the agricultural domain has become data-driven, allowing for well-timed and cost-effective farm management while remaining environmentally sustainable. Thus, the incorporation of Internet of Things in the agricultural domain is the need of the hour for developing countries whose gross domestic product primarily depends on the farming sector. It is worth highlighting that developing nations lack the infrastructure for precision agriculture; therefore, it has become necessary to come up with a methodological paradigm which can accommodate a complete model to connect ground sensors to the compute nodes in a cost-effective way by keeping the data processing limitations and constraints in consideration. In this regard, this review puts forward an overview of the state-of-the-art technologies deployed in precision agriculture for soil assessment and pollutant monitoring with respect to heavy metal in agricultural soil using various sensors. Secondly, this manuscript illustrates the processing of data generated from the sensors. In this regard, an optimized method of data processing derived from cloud computing has been shown, which is called edge computing. In addition to this, a new model of high-performance-based edge computing is also shown for efficient offloading of data with smooth workflow optimization. In a nutshell, this manuscript aims to open a new corridor for the farming sector in developing nations by tackling challenges and providing substantial consideration.

Application of Internet of Things (IoT) for Optimized Greenhouse Environments

“…The elimination of the technology constraints increases the potential areas of application of IoT, especially in the intelligent management of crops and agricultural machinery, the safety traceability of agricultural products, and pest identification. The positive assessment of the role of edge computing by Zhang, Cao, and Dong [21] is consistent with Akhtar et al [22] and O'Grady, Langton, and O'Hare [23]. Both studies concurred that edge computing would have a positive impact on the agricultural industry.…”

A Critical Review for Real-Time Continuous Soil Monitoring: Advantages, Challenges, and Perspectives

“…On one hand, data computing is brought closer to the data source, which greatly facilitates the development of delaysensitive applications. 136 On the other hand, the network traffic is largely reduced since the local processing avoids much data transmission, which remarkably saves the cost. 136 As a result, AI-enabled edge devices are getting popular.…”

“…136 On the other hand, the network traffic is largely reduced since the local processing avoids much data transmission, which remarkably saves the cost. 136 As a result, AI-enabled edge devices are getting popular. Even with minimal capability, this local processing can help a great deal to reduce data transmission costs by sending only the data that requires further processing.…”

Edge Computing Driven Data Sensing Strategy in the Entire Crop Lifecycle for Smart Agriculture

“…However, the agricultural IoT frameworks based on edge computing and related primary applications are still in the initial stages [25]. There have been few related studies on improving the parameter sensing performance and data value in the entire crop lifecycle using edge computing [26].…”

“…Munir et al [34] introduced edge computing nodes between traditional agricultural IoT sensing nodes and remote servers to optimize irrigation sensing data. Furthermore, in [26], the authors proposed a new data sensing model based on highperformance edge computing to realize efficient unloading of data and smooth workflow optimization for monitoring crop soil environment.…”