Abstract
Precision Agriculture (PA) is a management strategy that utilizes communication and information technology for farm management. It is a key to improve productivity by using the best agricultural practices and optimal usage of resources. Agriculture faces diverse challenges due to soil degradation, climate variation, and increasing costs. To unfold these challenges, PA uses Wireless Sensor Networks(WSNs) and exploits acquisition, communication, and processing of the data as basic enabling technologies to amplify the crop yield. Also, many other multidisciplinary technologies are supporting PA in finding the most novel use cases for PA. The use of Machine Learning (ML) and Artificial Intelligence (AI) has transformed PA at almost every level. The fog/edge paradigm is mitigating many challenges such as network bandwidth and security by bringing computation closer to the deployed network. At the same time, Software Defined Networks (SDN) brings flexibility, big data assists in handling data, and nano-technology plays a crucial part in driving the innovation in PA. This paper delves into ways these technologies are transforming PA in respective tracks, exhibiting the significance of integrating multidisciplinary approaches towards the future of PA. In addition to a comprehensive survey, this paper proposes a multidisciplinary architecture: AgriFusion, for efficient and cost-effective agriculture solutions. A list of industrial solutions for different aspects of farm management and their underlying focused technology have been highlighted. This can help to align research and industrial goals for PA. Furthermore, this paper defines a step approach to describe the performance dichotomy between resource availability and objectives for PA. In addition, solution architecture is proposed for designing Key Performance Indicators (KPI) in PA. In the end, some open research issues in implementing PA and respective future scopes have been presented.
Highlights
T HE Internet of Things (IoT) in recent years is directing a paradigm shift in all the areas of human-machine interaction
According to the new market research published by Meticulous, the agriculture IoT market is expected to grow at a Compound Annual Growth Rate (CAGR) of 15.2% from 2020 to reach $32.7 billion by 2027 [2]
Mekonnen et al [32] did the comprehensive review of Artificial Intelligence (AI) and Machine Learning (ML) techniques in Wireless Sensor Network (WSN) based data acquisition for Precision Agriculture (PA)
Summary
T HE Internet of Things (IoT) in recent years is directing a paradigm shift in all the areas of human-machine interaction. A WSN is a network formed by deploying sensors to collect and forward the data to the enterprise/cloud for further processing This precise data from the sensors, aerial devices, and IoT solutions are used for predicting climate change, increasing farm productivity with environmental sustainability, monitoring, and having a proactive reaction to crop performance. It helps in choosing a suitable crop by observing and measuring the demand or dependent factors. Precision agriculture is continuously evolving according to the advances in the underlying IoT technologies This evolution aims at achieving a set of key features to improve efficiency and boost crop yields. Structure of the paper and our contribution can be visualized through Figure 3
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