The use of sensors and the Internet of Things (IoT) is key to moving the world’s agriculture to a more productive and sustainable path. Recent advancements in IoT, Wireless Sensor Networks (WSN), and Information and Communication Technology (ICT) have the potential to address some of the environmental, economic, and technical challenges as well as opportunities in this sector. As the number of interconnected devices continues to grow, this generates more big data with multiple modalities and spatial and temporal variations. Intelligent processing and analysis of this big data are necessary to developing a higher level of knowledge base and insights that results in better decision making, forecasting, and reliable management of sensors. This paper is a comprehensive review of the application of different machine learning algorithms in sensor data analytics within the agricultural ecosystem. It further discusses a case study on an IoT based data-driven smart farm prototype as an integrated food, energy, and water (FEW) system.

The downtime of industrial machines, engines, or heavy equipment can lead to a direct loss of revenue. Accurate prediction of such failures using sensor data can prevent or reduce the downtime. With the availability of Internet of Things (IoT) technologies, it is possible to acquire the sensor data in real-time. Machine Learning and Deep Learning (DL) algorithms can then be used to predict the part and equipment failures, given enough historical data. DL algorithms have shown significant advances in problems where progress has eluded the practitioners and researchers for several decades. This paper reviews the DL algorithms used for predictive maintenance and presents a case study of engine failure prediction. We also discuss the current use of sensors in the industry and future opportunities for electrochemical sensors in predictive maintenance.

Globally, farmers are seeking advanced precision technology to help transform their practices into a more sustainable and productive agri-tech process. Accurate and real-time soil data has become one of the most valuable resources among farmers. Real-time soil sensor data can be exploited in manners that increase farm production and profit, maintain and increase product quality, promote food security, and ensure environmental protection. Researchers have already attempted to develop real-time in situ soil nutrient sensors based on optical and electrochemical techniques. Of these sensor systems, only a few of them are commercially available for monitoring. In this review, we present both available sensors and sensors under research in agriculture. Then briefly discuss both advantages and challenges to overcome in order to produce systems that deliver real-time quality soil information. Optical and electrochemical sensors are becoming less expensive to manufacture and can provide results that are comparable to laboratory soil analysis. Based on the literature presented here, there still exists a need to understand the effects of soil heterogeneity on the analytical performance of both electrochemical and optical systems when used in situ. By doing so, these sensors can be fully adopted as suitable commercial platforms. Overall, these sensors harness the potential to revolutionize decision management systems in agriculture as internet of things (IoT) soil nutrient sensors.

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Agriculture sector has been greatly influenced by the recent advances in electrical engineering. Nitrogen (N), from fertilizer, remains one of the largest input to surface and groundwater contamination, resulting in environmental and human health degradation. This paper explores the use of wireless potentiometry in field settings for in situ N monitoring. We report a disposable IoT gardening soil sheet, capable of analyzing real-time soil nitrate concentration during leaching and irrigation events. The nitrate doped polypyrrole ion selective electrode (N-doped PPy ISE) sensor array sheet features a fault tolerant circuit design multiplexed to an oxidation and reduction potentiometer that can rapidly detect nitrate levels in soil leachates. Measurement data are transmitted via Waspmote ZB Pro SMA 5dBi radio, 6600mAh rechargeable battery, 7.4-volt solar panel, and a Meshlium ZigBee PRO access point to cloud server and mobile device. This paper investigates the gardening IoT sheets as a viable tool for in situ nitrate mapping, and to potentially help everyday home and commercial gardeners reduce excessive fertilizer application.