A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure

Data-Driven Analysis of Privacy Policies Using LexRank and KL Summarizer for Environmental Sustainability

Natural language processing (NLP) is a field in machine learning that analyses and manipulate huge amounts of data and generates human language. There are a variety of applications of NLP such as sentiment analysis, text summarization, spam filtering, language translation, etc. Since privacy documents are important and legal, they play a vital part in any agreement. These documents are very long, but the important points still have to be read thoroughly. Customers might not have the necessary time or the knowledge to understand all the complexities of a privacy policy document. In this context, this paper proposes an optimal model to summarize the privacy policy in the best possible way. The methodology of text summarization is the process where the summaries from the original huge text are extracted without losing any vital information. Using the proposed idea of a common word reduction process combined with natural language processing algorithms, this paper extracts the sentences in the privacy policy document that hold high weightage and displays them to the customer, and it can save the customer’s time from reading through the entire policy while also providing the customers with only the important lines that they need to know before signing the document. The proposed method uses two different extractive text summarization algorithms, namely LexRank and Kullback Leibler (KL) Summarizer, to summarize the obtained text. According to the results, the summarized sentences obtained via the common word reduction process and text summarization algorithms were more significant than the raw privacy policy text. The introduction of this novel methodology helps to find certain important common words used in a particular sector to a greater depth, thus allowing more in-depth study of a privacy policy. Using the common word reduction process, the sentences were reduced by 14.63%, and by applying extractive NLP algorithms, significant sentences were obtained. The results after applying NLP algorithms showed a 191.52% increase in the repetition of common words in each sentence using the KL summarizer algorithm, while the LexRank algorithm showed a 361.01% increase in the repetition of common words. This implies that common words play a large role in determining a sector’s privacy policies, making our proposed method a real-world solution for environmental sustainability.

Promoting agri-food systems resilience through ICT in developing countries amid COVID-19

An increasing body of literature has demonstrated COVID-19's harmful impact on agri-food systems, which are a major source of livelihood for millions of people worldwide. Information and communication technology (ICT) has been playing an increasing role in enhancing agri-food systems' resilience amid COVID-19. In this study, the PRISMA approach was employed to perform a systematic review of the literature from January 2020 to December 2021 on the overall impact of COVID-19 on agri-food system networks and ICT's role in enhancing agri-food system resilience in developing countries. This study reveals that COVID-19 has posed abundant obstacles to agri-food systems actors, including a lack of inputs, technical support, challenges to selling the product, transportation barriers, and low pricing. These impediments result in insufficient output, unforeseen stock, and revenue loss. COVID-19's restrictions have caused a significant food deficit by disrupting the demand and supply sides of the agri-food system networks. A high number of small-scale farmers have had to deal with food insecurity. As a result of the cumulative effects, actors in the agri-food system are getting less motivated to continue producing. This study also argues that many challenges in the agri-food systems can be overcome using ICTs, including maintaining precise farm management, product marketing, and access to production inputs. To assist stakeholders in coping with, adapting to, and building resilience in the agri-food system networks, this article emphasizes the critical need to turn to and expand the application of advanced agricultural ICTs to meet the world's growing needs for food production and to ensure the resilience and sustainability of farming systems, particularly in the face of a pandemic like COVID-19.

The carbon emission reduction effect of digital agriculture in China

Carbon emission reduction is gaining increasing attention worldwide. This paper focuses on how the development of digital agriculture contributes to agricultural carbon emission reduction. To this end, the spatial characteristics, spillover effects, and driving factors of digital agriculture on agricultural carbon emissions are explored using panel data of 31 regions in China from 2011 to 2019 using a spatial econometric model and STIRPAT model with the extension of an ARDL method that was utilized to demonstrate the linkage amid variables. The results show that digital agriculture development reduces agricultural carbon emissions. Firstly, the results remain robust after estimation using the replacement weight method and the explanatory variable substitution method. Agricultural technological progress, agricultural industry structure, and rural education level all contribute to the reduction of agricultural carbon emissions in a region. Secondly, agricultural carbon emissions in the neighboring regions have a negative relationship with the agricultural industry structure in the region and a positive relationship with rural education level and agricultural technological level. Finally, strengthening the exchange of digital agriculture between regions and leveraging the intermediary effect of digital inclusive finance can effectively enhance the carbon emission reduction effect.

Experimenting Agriculture 4.0 with Sensors: A Data Fusion Approach between Remote Sensing, UAVs and Self-Driving Tractors

Geomatics is important for agriculture 4.0; in fact, it uses different types of data (remote sensing from satellites, Unmanned Aerial Vehicles-UAVs, GNSS, photogrammetry, laser scanners and other types of data) and therefore it uses data fusion techniques depending on the different applications to be carried out. This work aims to present on a study area concerning the integration of data acquired (using data fusion techniques) from remote sensing techniques, UAVs, autonomous driving machines and data fusion, all reprocessed and visualised in terms of results obtained through GIS (Geographic Information System). In this work we emphasize the importance of the integration of different methodologies and data fusion techniques, managing data of a different nature acquired with different methodologies to optimise vineyard cultivation and production. In particular, in this note we applied (focusing on a vineyard) geomatics-type methodologies developed in other works and integrated here to be used and optimised in order to make a contribution to agriculture 4.0. More specifically, we used the NDVI (Normalized Difference Vegetation Index) applied to multispectral satellite images and drone images (suitably combined) to identify the vigour of the plants. We then used an autonomous guided vehicle (equipped with sensors and monitoring systems) which, by estimating the optimal path, allows us to optimise fertilisation, irrigation, etc., by data fusion techniques using various types of sensors. Everything is visualised on a GIS to improve the management of the field according to its potential, also using historical data on the environmental, climatic and socioeconomic characteristics of the area. For this purpose, experiments of different types of Geomatics carried out individually on other application cases have been integrated into this work and are coordinated and integrated here in order to provide research/application cues for Agriculture 4.0.

A Bibliometric Review of the Use of Unmanned Aerial Vehicles in Precision Agriculture and Precision Viticulture for Sensing Applications

This review focuses on the use of unmanned aerial vehicles (UAVs) in precision agriculture, and specifically, in precision viticulture (PV), and is intended to present a bibliometric analysis of their developments in the field. To this aim, a bibliometric analysis of research papers published in the last 15 years is presented based on the Scopus database. The analysis shows that the researchers from the United States, China, Italy and Spain lead the precision agriculture through UAV applications. In terms of employing UAVs in PV, researchers from Italy are fast extending their work followed by Spain and finally the United States. Additionally, the paper provides a comprehensive study on popular journals for academicians to submit their work, accessible funding organizations, popular nations, institutions, and authors conducting research on utilizing UAVs for precision agriculture. Finally, this study emphasizes the necessity of using UAVs in PV as well as future possibilities.