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 nanotechnology 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.

The spectrum of Internet of Things (IoT) applications is exponentially growing, driving the demand for better energy performance metrics. In conjunction, Low Power Wide Area Networks (LPWAN) have evolved as long-range connectivity enabler with low management cost. The integration of LPWAN communication assists in reliable IoT operation with extended lifetime. Notable LPWAN technologies that contend for many of the IoT applications are LoRaWAN, DASH7, Sigfox, and NB-IoT. Most of the end-devices such as sensors and actuators are battery powered, therefore investigating energy consumption becomes crucial. To estimate the consumed power, it is important to analyze the energy consumption in wireless communication. This paper describes an empirical evaluation of energy consumption for LPWAN wireless technologies. We measure the current consumption of LoRaWAN, DASH7, Sigfox, and NB-IoT and derive the respective battery lifetime. These measurements help to quantify the energy performance of different protocols. We observe that LoRaWAN and DASH7 are more energy efficient when compared to Sigfox and NB-IoT. Finally, a case study on energy consumption is done on precision agriculture in the greenhouse, showing that battery lifetime in real applications can drop significantly from the ideal case. These results can be used for increasing the effectiveness of the IoT application by selecting the right technology and battery capacity.

The technology development in wireless sensor network (WSN) offers a sustainable solution towards precision agriculture (PA) in greenhouses. It helps to effectively use the agricultural resources and management tools and monitors different parameters to attain better quality yield and production. WSN makes use of Low-Power Wide-Area Networks (LPWANs), a wireless technology to transmit data over long distances with minimal power consumption. LoRaWAN is one of the most successful LPWAN technologies despite its low data rate and because of its low deployment and management costs. Greenhouses are susceptible to different types of interference and diversification, demanding an improved WSN design scheme. In this paper, we contemplate the viable challenges for PA in greenhouses and propose the successive steps essential for effectual WSN deployment and facilitation. We performed a real-time, end-to-end deployment of a LoRaWAN-based sensor network in a greenhouse of the ’Proefcentrum Hoogstraten’ research center in Belgium. We have designed a dashboard for better visualization and analysis of the data, analyzed the power consumption for the LoRaWAN communication, and tried three different enclosure types (commercial, simple box and airflow box, respectively). We validated the implications of real-word challenges on the end-to-end deployment and air circulation for the correct sensor readings. We found that temperature and humidity have a larger impact on the sensor readings inside the greenhouse than we initially thought, which we successfully solved through the airflow box design.

Internet of Things (IoT) technology is shaping different aspects of agriculture management strategies. Precision Agriculture (PA) can be improved by the provision of adequate nutrients and effective control of pests and diseases. Effective analysis and utilization of IoT technologies can help in PA. IoT stipulates connectivity technology between multiple devices but limited battery energy remains as a foremost concern. This battery constraint needs a combined approach of energy-efficient and energy harvesting mechanisms for long term monitoring and data collection. This paper outlines research questions for energy efficient wireless communication on the basis of literature evaluation. Potential research design and approach for power reduction are explored using our pilot LoRa based IoT deployment in a greenhouse. This pilot study helps in identifying challenges and limitations of IoT connectivity in greenhouse. We also discuss the possible research outlook to increase the opportunities in saving energy and efficient wireless data communication.

In recent years, greenhouse-based precision agriculture (PA) has been strengthened by utilization of Internet of Things applications and low-power wide area network communication. The advancements in multidisciplinary technologies such as artificial intelligence (AI) have created opportunities to assist farmers further in detecting disease and poor nutrition of plants. Neural networks and other AI techniques need an initial set of measurement campaigns along with extensive datasets as a training set to baseline and evolve different applications. This paper presents LoRaWAN-based greenhouse monitoring datasets over a period of nine months. The dataset has both the network and sensing information from multiple sensor nodes for tomato crops in two different greenhouse environments. The goal is to provide the research community with a dataset to evaluate performance of LoRaWAN inside a greenhouse and develop more efficient PA monitoring techniques. In this paper, we carried out an exploratory data analysis to infer crop growth by analyzing just the LoRaWAN signals and without inclusion of any extra hardware. This work uses a multilayer perceptron artificial neural network to predict the weekly plant growth, trained using RSSI value from sensor data and manual measurement of plant height from the greenhouse. We developed this proof of concept of joint communication and sensing by using generated dataset from the “Proefcentrum Hoogstraten” greenhouse in Belgium. Results for the proposed method yield a root mean square error of 10% in detecting the average plant height inside a greenhouse. In future, we can use this concept of landscape sensing for different supplementary use-cases and to develop optimized methods.