Implementing the Sustainable Development Goals with a digital platform: Experiences from the vitivinicultural sector

none5noEmerging technologies, such as Digital Platforms, Internet of Things, remote sensing and Big Data, are going to significantly influence the achievement of the 17 Sustainable Development Goals (SDGs) targets, pursued by all United Nations Member States starting from 2015. As the whole agricultural sector is transforming in a more knowledge-intensive system, precision agriculture could play a significant role to achieve the SDGs, by reducing environmental impacts of agriculture and farming practices, increasing the profitability of the farm and thus improving the quality of life for farmers Based on these premises, the aim of this article is to present VITIS, a digital platform, for the management of vineyard water and nitrogen stress, developed by the Operational Group SMART VITIS and tested in 4 pilots located in Marche Region. All the functions and modules of the platform were built by following a Design Thinking approach. This approach started from the analysis of the needs of the winegrowers, the end-user of the solution. While a focus group, made of agri-experts was conducted to receive feedback from the test phase of the platform. This study illustrates how this approach can be a useful tool to develop targeted digital solutions for farmers with low digital skills.openBucci G.; Bentivoglio D.; Belletti M.; Finco A.; Anceschi E.Bucci, G.; Bentivoglio, D.; Belletti, M.; Finco, A.; Anceschi, E

A Fog Computing Framework for Intrusion Detection of Energy-Based Attacks on UAV-Assisted Smart Farming

Precision agriculture and smart farming have received significant attention due to the advancements made in remote sensing technology to support agricultural efficiency. In large-scale agriculture, the role of unmanned aerial vehicles (UAVs) has increased in remote monitoring and collecting farm data at regular intervals. However, due to an open environment, UAVs can be hacked to malfunction and report false data. Due to limited battery life and flight times requiring frequent recharging, a compromised UAV wastes precious energy when performing unnecessary functions. Furthermore, it impacts other UAVs competing for charging times at the station, thus disrupting the entire data collection mechanism. In this paper, a fog computing-based smart farming framework is proposed that utilizes UAVs to gather data from IoT sensors deployed in farms and offloads it at fog sites deployed at the network edge. The framework adopts the concept of a charging token, where upon completing a trip, UAVs receive tokens from the fog node. These tokens can later be redeemed to charge the UAVs for their subsequent trips. An intrusion detection system is deployed at the fog nodes that utilize machine learning models to classify UAV behavior as malicious or benign. In the case of malicious classification, the fog node reduces the tokens, resulting in the UAV not being able to charge fully for the duration of the trip. Thus, such UAVs are automatically eliminated from the UAV pool. The results show a 99.7% accuracy in detecting intrusions. Moreover, due to token-based elimination, the system is able to conserve energy. The evaluation of CPU and memory usage benchmarks indicates that the system is capable of efficiently collecting smart-farm data, even in the presence of attacks

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others