An Approach Based on Fog Computing for Providing Reliability in IoT Data Collection: A Case Study in a Colombian Coffee Smart Farm

From crop specific to variety specific in crop modeling for the smart farm: A case study with blueberry

Facility cultivation has been evolved from greenhouses to smart farms using artificial intelligence (AI) that simulates big data to maximize production. However, the big data for AI in smart farm is not studied well; the effect of differences among varieties within a crop remains unclear. Therefore, the response of two varieties of blueberry, ‘Suziblue’ and ‘Star’, to light was tested using SAPD meter in order to demonstrate the environmental responses could be different among varieties within the same species. The results showed that those two varieties had significant differences in SPAD values based on the leaf’s position and time, whereas ‘Star’ did not. This indicates that the effect of light depends on the variety, which implies that other traits and other crops may show similar differences. These results are based on a simple experiment. However, it is enough to elucidate that it is extremely important to characterize responses to the environment not only for each crop but also for each variety to collect data for smart farming to increase accuracy for modeling; consequently, to maximize the efficiency of these facilities.

A Systematic Survey on the Role of Cloud, Fog, and Edge Computing Combination in Smart Agriculture

Cloud Computing is a well-established paradigm for building service-centric systems. However, ultra-low latency, high bandwidth, security, and real-time analytics are limitations in Cloud Computing when analysing and providing results for a large amount of data. Fog and Edge Computing offer solutions to the limitations of Cloud Computing. The number of agricultural domain applications that use the combination of Cloud, Fog, and Edge is increasing in the last few decades. This article aims to provide a systematic literature review of current works that have been done in Cloud, Fog, and Edge Computing applications in the smart agriculture domain between 2015 and up-to-date. The key objective of this review is to identify all relevant research on new computing paradigms with smart agriculture and propose a new architecture model with the combinations of Cloud–Fog–Edge. Furthermore, it also analyses and examines the agricultural application domains, research approaches, and the application of used combinations. Moreover, this survey discusses the components used in the architecture models and briefly explores the communication protocols used to interact from one layer to another. Finally, the challenges of smart agriculture and future research directions are briefly pointed out in this article.

Protocol-Independent Data Acquisition for Precision Farming

This paper proposes an optimal design of network and a routing mechanism that is independent from fixed protocols. It provides an optimized route for diversified mesh network, which can support interorganizational communication in a large-scale operation. Decentralization of the system ensures that every protocol acts independently and selects the best optimal path during transmission of data without modifying their architecture and technology. Incorporation of definite source configuration improves the mobility of the systems. Each sensor is individually processed to balance the data load and prevent congestion. Simple transmit-receive test is performed by circulating messages of increasing size between end sensors and network destination. The proposed technique is considered to be effective in terms of interoperability speed, data accuracy and bit error rate (BER) with an increment of 27.13%, 99.98%, and 15.12%, respectively. Finally, the test demonstrates its expediency in terms of adaptability and scalability.