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In this work, the performance of the TEROS 12 electromagnetic sensor, which measures volumetric soil water content (θ), bulk soil electrical conductivity (σb), and temperature, is examined for a number of different soils, different θ and different levels of the electrical conductivity of the soil solution (ECW) under laboratory conditions. For the above reason, a prototype device was developed including a low-cost microcontroller and suitable adaptation circuits for the aforementioned sensor. Six characteristic porous media were examined in a θ range from air drying to saturation, while four different solutions of increasing Electrical Conductivity (ECw) from 0.28 dS/m to approximately 10 dS/m were used in four of these porous media. It was found that TEROS 12 apparent dielectric permittivity (εa) readings were lower than that of Topp's permittivity–water content relationship, especially at higher soil water content values in the coarse porous bodies. The differences are observed in sand (S), sandy loam (SL) and loam (L), at this order. The results suggested that the relationship between experimentally measured soil water content (θm) and εa0.5 was strongly linear (0.869 < R2 < 0.989), but the linearity of the relation θm-εa0.5 decreases with the increase in bulk EC (σb) of the soil. The most accurate results were provided by the multipoint calibration method (CAL), as evaluated with the root mean square error (RMSE). Also, it was found that εa degrades substantially at values of σb less than 2.5 dS/m while εa returns to near 80 at higher values. Regarding the relation εa-σb, it seems that it is strongly linear and that its slope depends on the pore water electrical conductivity (σp) and the soil type. [ABSTRACT FROM AUTHOR]

The continuous growth of the urban electric vehicles market and the rapid progress of the electronics industry create positive prospects towards fostering the development of autonomous robotic solutions for covering critical production sectors. Agriculture can be seen as such, as its digital transformation is a promising necessity for protecting the environment, and for tackling the degradation of natural resources and increasing nutritional needs of the population on Earth. Many studies focus on the potential of agricultural robotic vehicles to perform operations of increased intelligence. In parallel, the study of the activity footprint of these vehicles can be the basis for supervising, detecting the malfunctions, scaling up, modeling, or optimizing the related operations. In this regard, this work, employing a prototype lightweight autonomous electric cargo vehicle, outlines a simple and cost-effective mechanism for a detailed robot's power consumption logging. This process is conducted at a fine time granularity, allowing for detailed tracking. The study also discusses the robot's energy performance across various typical agricultural field operation scenarios. In addition, a comparative analysis has been conducted to evaluate the performance of two different types of batteries for powering the robot for all the operation scenarios. Even non-expert users can conduct the field operation experiments, while directions are provided for the potential use of the data being collected. Given the linear relationship between the size and the consumption of electric robotic vehicles, the energy performance of the prototype agricultural cargo robot can serve as a basis for various studies in the area. [ABSTRACT FROM AUTHOR]

The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs on Earth and the degradation of natural resources. Toward this direction, the availability of innovative electronic components and of the accompanying software programs can be exploited to detect malfunctions in typical agricultural equipment, such as water pumps, thereby preventing potential failures and water and economic losses. In this context, this article highlights the steps for adding intelligence to sensors installed on pumps in order to intercept and deliver malfunction alerts, based on cheap in situ microcontrollers, sensors, and radios and easy-to-use software tools. This involves efficient data gathering, neural network model training, generation, optimization, and execution procedures, which are further facilitated by the deployment of an experimental platform for generating diverse disturbances of the water pump operation. The best-performing variant of the malfunction detection model can achieve an accuracy rate of about 93% based on the vibration data. The system being implemented follows the on-device intelligence approach that decentralizes processing and networking tasks, thereby aiming to simplify the installation process and reduce the overall costs. In addition to highlighting the necessary implementation variants and details, a characteristic set of evaluation results is also presented, as well as directions for future exploitation. [ABSTRACT FROM AUTHOR]

The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs of the population on Earth and the degradation of natural resources. Focusing on the "hot" area of natural resource preservation, the recent appearance of more efficient and cheaper microcontrollers, the advances in low-power and long-range radios, and the availability of accompanying software tools are exploited in order to monitor water consumption and to detect and report misuse events, with reduced power and network bandwidth requirements. Quite often, large quantities of water are wasted for a variety of reasons; from broken irrigation pipes to people's negligence. To tackle this problem, the necessary design and implementation details are highlighted for an experimental water usage reporting system that exhibits Edge Artificial Intelligence (Edge AI) functionality. By combining modern technologies, such as Internet of Things (IoT), Edge Computing (EC) and Machine Learning (ML), the deployment of a compact automated detection mechanism can be easier than before, while the information that has to travel from the edges of the network to the cloud and thus the corresponding energy footprint are drastically reduced. In parallel, characteristic implementation challenges are discussed, and a first set of corresponding evaluation results is presented. [ABSTRACT FROM AUTHOR]

Biomimetics is the interdisciplinary cooperation of biology and technology that offers solutions to practical problems by analyzing biological systems and transferring their principles into applications. This review article focused on biomimetic innovations, including bio-inspired soft robots and swarm robots that could serve multiple functions, including the harvesting of fruits, pest control, and crop management. The research demonstrated commercially available biomimetic innovations, including robot bees by Arugga AI Farming and the Robotriks Traction Unit (RTU) precision farming equipment. Additionally, soft robotic systems have made it possible to mitigate the risk of surface bruises, rupture, the crushing destruction of plant tissue, and plastic deformation in the harvesting of fruits with a soft rind such as apples, cherries, pears, stone fruits, kiwifruit, mandarins, cucumbers, peaches, and pome. Even though the smart farming technologies, which were developed to mimic nature, could help prevent climate change and enhance the intensification of agriculture, there are concerns about long-term ecological impact, cost, and their inability to complement natural processes such as pollination. Despite the problems, the market for bio-inspired technologies with potential agricultural applications to modernize farming and solve the abovementioned challenges has increased exponentially. Future research and development should lead to low-cost FEA robotic grippers and FEA-tendon-driven grippers for crop harvesting. In brief, soft robots and swarm robotics have immense potential in agriculture. [ABSTRACT FROM AUTHOR]

The focus of this review paper was to investigate innovations currently employed to capture and use greenhouse gases produced within livestock farms for energy production and expected future directions. The methods considered for data collection regarded a systematic review of the literature, where 50 journal articles were critically reviewed. The main findings identified that the conventional method used in transforming livestock agriculture greenhouse gases into energy regards the combustion of biogas. However, emerging methods encompass microbial fuel cells, dry biogas reforming, steam biogas reforming, auto thermal Chemical Looping Reforming (CLRa), and gas-to-liquid methods that convert methane to liquid hydrocarbons. The conclusions from the review are that there is a potential to integrate these methods in livestock agriculture in order to generate energy from greenhouse emissions and reduce the reliance on fossil fuels. [ABSTRACT FROM AUTHOR]

• A spraying robotic platform is enhanced for supporting plants' thermal imagery. • Destined for both scientists and farmers exploiting preexistent equipment. • Autonomous operations are added, using GPS, IMUs, machine vision components. • The layout guarantees almost non-invasive operation and low noise image capturing. • Platform is fully electric, uses solar panels, provides detailed monitoring data. The recent developments in electronics have made available a variety of systems at very appealing quality, cost and size levels. These conditions are not left unexploited by modern agriculture that seeks to tackle the increased global food demand and the degradation of available resources. Indeed, the precision agriculture sector is flourishing assisted by outstanding innovations, like the UAVs, that can carry a plethora of sensors. It must be noted though that, despite the progress being achieved, several high-quality measurements still demand expensive, often pre-existing but heavy instruments, and meticulous time-consuming operations. The latter requirements are not always in-line with the flight duration and cargo weight limitations of a typical UAV. This gap is apparent when scientists have to apply precise thermal imagery methods to support actions like accurate plant inspection or early disease detection. In order to provide a satisfactory solution, an experimental electric robotic platform, mainly destined for spraying operations, was enhanced so as to improve its functions and to host a high-quality thermal/optical camera. This platform has been equipped with a set of complementary electronic systems so as to provide fluent and robust semi-autonomous remote monitoring operation. The overall layout is lightweight, eliminating soil compaction, reducing vibration and thermal footprint, guarantying almost non-invasive operation and image capturing at very low noise levels. The whole approach is targeting to combine innovative but easy-to-find assistive components of affordable cost in local markets, emphasizing the challenges towards a really functioning solution accompanied by detailed performance data. The overall study is targeting the rapidly growing robotic vehicles market. The proposed platform is following the modern sustainability objectives as it is fully electric and can be assisted by solar panels, for increased autonomy. Finally, insight is given for future improvements and exploitation. [ABSTRACT FROM AUTHOR]

Long Range (LoRa) systems have recently attracted significant attention within the research community as well as for commercial use due to their ability to transmit data over long distances at a relatively low energy cost. In this study, new results for the bit error rate performance of Long Range (LoRa) systems operating in the presence of Rayleigh, Rice, Nakagami-m, Hoyt, η - μ and generalized fading channels are presented. Specifically, we propose novel exact single integral expressions as well as simple, accurate expressions that yield tight results in the entire signal-to-noise ratio (SNR) region. The validity of our newly derived formulas is substantiated by comparing numerically evaluated results with equivalent ones, obtained using Monte-Carlo simulations and exact analytical expressions. [ABSTRACT FROM AUTHOR]