Edge computing offers a potentially tractable model for mainstreaming smart agriculture. There are dozens of smart farming and farming applications, from monitoring climate change and monitoring the health of crops and livestock, to automating or even complex greenhouses complete IoT-enabled farm management solutions. Ensuring food security remains a global challenge; Thus, this is the specific goal of the United Nations 2030 Sustainable Development Goals. The successful establishment of productive and sustainable agricultural systems around the world will lay the foundation for solving this problem. Smart agriculture is often seen as one of the key enablers when considering the dual goal of eradicating world hunger and malnutrition. The practical implementation, deployment and implementation of smart agricultural systems remains distant due to a combination of technological, social and economic factors. There are synergies that, if used effectively, will increase the penetration of smart farming technologies across international boundaries between majorities and minorities.
Intelligent agriculture involves the use of various information and communication technologies (ICTs) to increase productivity that was sustainable and economically viable. The system is technology agnostic; suggests establishing a set of technologies that can be used according to the context. Such technologies can be implemented anywhere in the agricultural value chain from producer to consumer. At the farm level, farmers have been focusing on the opportunities offered to reduce costs while being more efficient. The introduction of ICT in agriculture faces many challenges. The benefits of smart farming are well documented - cost effectiveness, sustainability, etc. One common theme emerges; Smart farming services almost always rely on open internet access. Edge Computing provides a way in which the farming community can access and make better use of intelligent farming services. This may help alleviate the Internet problem, but not eliminate it. Integrating the Edge system into their designs will pose challenges for service providers. Indeed, the agricultural sector, especially at the farm level, is a compelling example of a superior computing validation for intelligent service delivery. Thus, this article attempts to examine how edge computing is being used by the research community in the broader agricultural sector.
Food poverty: an internal contradiction?Obesity is a global epidemic; traditionally associated with adults, prevalence in children is increasing rapidly. According to WHO1, more than 1.9 billion adults aged 18 and over were overweight in 2016; of these, more than 650 million adults were obese. For children, about 41 million children under the age of five are overweight or obese. Obesity is on the rise in low- and middle-income countries. The science of obesity is complicated; it is caused by many factors including genetics, hormones, and environment. Interestingly, obesity can be seen as part of food poverty; Adherence to a healthy diet is often costly for low-income populations in high-income countries, contributing to the relative prevalence of obesity and poverty. The second tension occurs as perceived food waste and spoilage. It is estimated that a third of all food produced for human consumption is lost or wasted throughout the food supply chain, from production to consumption. Such losses equate to 1.3 billion tonnes/year; in fact, a quarter of the calories produced for human consumption become wasteful food. To ensure food availability The causes of insecure food production and distribution are complex and multifaceted. Many factors, including political instability, conflict, poor economic prospects and natural disasters, contribute to this.
In terms of natural disasters, floods, droughts and tropical storms have the greatest impact on food production (FAO, 2015); drought in particular causes more than 80% of the total damage and losses in agriculture, especially in the livestock and crop sectors. Since there is no single cause of food supply problems, there can be no single solution; however, a mixed strategic position should be adopted. Towards functional smart agricultureHistorically, the adoption of ICT in agriculture has been very slow; various reasons are explained why this happened, but the biggest obstacle is the lack of internal facilities. Social and economic problems seem to be the cause. However, the value proposition is important, and this remains unclear to many US farmers. Thus, it is the responsibility of policy makers to ensure that value propositions are meaningful not only to farmers, but to all participants in the value chain. The World Economic Forum (WEF) estimates that if IoT solutions are deployed in 50–75% of supply chains in developed countries by 2020, 10–10 million tonnes of food will be consumed. 50 can be saved (WEF, 2018). . . . . Smart farming provides a human look at how technology will change the farming process. Food production starts on the farm; as such, sustainable global food solutions must also begin on the farm and with the farmer. One global problem at the farm level will not appear; instead, smart farming should support a variety of agricultural practices.
intelligent farmingICT technology continues to make its way into agriculture, and as a result, several approaches have been identified to handle this phenomenon. Smart farming, digital farming, and e-agriculture are some of the terms found in academic journals and the popular press. While each method is often used interchangeably, each method can have nuances depending on the group of end users. One of the most commonly used terms is precision farming (PA), especially when referring to farm-level operations; the ubiquity of this process has been demonstrated in several farming systems. Positioning methodsSatellite positioning using GPS, Galileo and similar systems provides the basis for many intelligent services. In practice, the signal must be amplified to provide the required ground accuracy. Real-time kinematics (RTK) should be used to ensure the accuracy of the centimeter; this requires the receiver on agricultural vehicles to be suitably equipped. Since the accuracy of RTK is proportional to the distance from the base station, it may be necessary to place the RTK base station on the farm. Positional accuracy is critical for automated driving and steering, yield monitoring and variable rate technologies.