Agricultural Big Data Architectures in the Context of Climate Change: A Systematic Literature Review

Abstract

Climate change is currently one of agriculture’s main problems in achieving sustainability. It causes drought, increased rainfall, and increased diseases, causing a decrease in food production. In order to combat these problems, Agricultural Big Data contributes with tools that improve the understanding of complex, multivariate, and unpredictable agricultural ecosystems through the collection, storage, processing, and analysis of vast amounts of data from diverse heterogeneous sources. This research aims to discuss the advancement of technologies used in Agricultural Big Data architectures in the context of climate change. The study aims to highlight the tools used to process, analyze, and visualize the data, to discuss the use of the architectures in crop, water, climate, and soil management, and especially to analyze the context, whether it is in Resilience Mitigation or Adaptation. The PRISMA protocol guided the study, finding 33 relevant papers. However, despite advances in this line of research, few papers were found that mention architecture components, in addition to a lack of standards and the use of reference architectures that allow the proper development of Agricultural Big Data in the context of climate change.

Analysis of the development of fruit trees diseases using modified analytical model of fuzzy c-means method

“…This makes it possible to calculate the center of each cluster using (7). (7) The proposed fuzzy clustering model has a distinctive feature, which is that the distance measure [22] - [25] which should take into account both local and non-local information, is formalized in: (8) where -distances, taking into account the influence of local information, and distances, taking into account the influence of non-local information; is a parameter ranging from 0 to 1, a Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  weight factor which is determined by the user when solving the problem and can be corrected when an unsatisfactory solution is obtained. A larger value is taken to make the contribution of the distance to the local information larger, and a smaller value is taken to reduce the contribution of the non-local information distance.…”

“…The widespread use of the concept of creating a smart garden provides the ability to conduct an operational analysis of soil and climatic conditions based on the processing of big data [8]- [11] obtained from sensors installed both in the vicinity of the root system of a single tree, and directly on its trunk, to carry out the application of fertilizers of various types depending on the characteristics of a particular tree, carry out preventive measures to combat pests and diseases of fruit plants in a smart garden, based on the analysis of graphic information, which is represented by images of tree leaves. At the same time, issues related to the organization and storage of large volumes of both data and graphical choice of processing graphic information are of particular importance, the analysis of which allows you to take prompt measures in the process of fruit growth, taking into account the state of fruit trees.…”

Secure IoT communication: Implementing a One-Time-Pad Protocol with True Random Numbers and Secure Multiparty Sums

“…The increasing prevalence of IoT in our daily lives opens a wealth of opportunities to create new services; many of them personalized and customized for individuals, provided that privacy of individual and sensible data is granted in the process of constructing digests that can be used for public benefit, such as planning, policy making [1], smart agriculture [2], and health care [3], among others.…”

Agroclimatic and Phytosanitary Events and Emerging Technologies for Their Identification in Avocado Crops: A Systematic Literature Review

“…Some literature reviews have been conducted related to technology applications in the agricultural sector in general, but these studies do not address the specific management of pests, diseases, and climate changes in avocado crops using emerging technologies. Cravero et al conducted three literature reviews associated with big data (BD) in agriculture [22,27,28]. The first review aimed to examine the advancements of technologies employed in BD architectures for agriculture, with a focus on climate change.…”

“…The first review aimed to examine the advancements of technologies employed in BD architectures for agriculture, with a focus on climate change. The study highlights the tools utilized for processing, analyzing, and visualizing crop data, as well as the implementation of crop data architectures for effective monitoring of the weather, water, and soil conditions [28]. Following the same approach, the second review supplied insights into the utilization of BD and ML in the agricultural domain, without specifically addressing the adaptations and design of architectures for these systems to operate effectively in agricultural applications.…”