Geosensors to Support Crop Production: Current Applications and User Requirements

Sensor technology, which benefits from high temporal measuring resolution, real-time data transfer and high spatial resolution of sensor data that shows in-field variations, has the potential to provide added value for crop production. The present paper explores how sensors and sensor networks have been utilised in the crop production process and what their added-value and the main bottlenecks are from the perspective of users. The focus is on sensor based applications and on requirements that users pose for them. Literature and two use cases were reviewed and applications were classified according to the crop production process: sensing of growth conditions, fertilising, irrigation, plant protection, harvesting and fleet control. The potential of sensor technology was widely acknowledged along the crop production chain. Users of the sensors require easy-to-use and reliable applications that are actionable in crop production at reasonable costs. The challenges are to develop sensor technology, data interoperability and management tools as well as data and measurement services in a way that requirements can be met, and potential benefits and added value can be realized in the farms in terms of higher yields, improved quality of yields, decreased input costs and production risks, and less work time and load

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

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

Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

As the global population continues to increase, projected to reach an estimated 9.7 billion people by 2050, there will be a growing demand for food production and agricultural resources. Transition toward Agriculture 4.0 is expected to enhance agricultural productivity through the integration of advanced technologies, increase resource efficiency, ensure long-term food security by applying more sustainable farming practices, and enhance resilience and climate change adaptation. By integrating technologies such as ground IoT sensing and remote sensing, via both satellite and Unmanned Aerial Vehicles (UAVs), and exploiting data fusion and data analytics, farming can make the transition to a more efficient, productive, and sustainable paradigm. The present work performs a systematic literature review (SLR), identifying the challenges associated with UAV, Satellite, and Ground Sensing in their application in agriculture, comparing them and discussing their complementary use to facilitate Precision Agriculture (PA) and transition to Agriculture 4.0

Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis

Smart Farming (SF) is an emerging technology in the current agricultural landscape. The aim of Smart Farming is to provide tools for various agricultural and farming operations to improve yield by reducing cost, waste, and required manpower. SF is a data-driven approach that can mitigate losses that occur due to extreme weather conditions and calamities. The influx of data from various sensors, and the introduction of information communication technologies (ICTs) in the field of farming has accelerated the implementation of disruptive technologies (DTs) such as machine learning and big data. Application of these predictive and innovative tools in agriculture is crucial for handling unprecedented conditions such as climate change and the increasing global population. In this study, we review the recent advancements in the field of Smart Farming, which include novel use cases and projects around the globe. An overview of the challenges associated with the adoption of such technologies in their respective regions is also provided. A brief analysis of the general sentiment towards Smart Farming technologies is also performed by manually annotating YouTube comments and making use of the pattern library. Preliminary findings of our study indicate that, though there are several barriers to the implementation of SF tools, further research and innovation can alleviate such risks and ensure sustainability of the food supply. The exploratory sentiment analysis also suggests that most digital users are not well-informed about such technologies

Gestione sostenibile del vigneto mediante Data Science e Big Data Management

Negli ultimi anni, la ricerca in ambito viticolo (Vitis vinifera L.) è stata notevolmente influenzata dalla necessità duplice di rispondere alla crescente domanda di prodotto ad elevati standard qualitativi e a mediare criticità derivanti dagli effetti del cambiamento climatico. Alla base della mediazione di tali fattori risulta fondamentale una ricalibrazione della gestione del vigneto, spostandosi da un approccio convenzionale che prevede una sua gestione come unità omogenea, verso uno che tenga in considerazione le sue discontinuità spaziali legate alle peculiarità pedoclimatiche e alle variabili biotiche, le quali, avendo riflessi eterogenei sul ciclo biologico della vite, determinano un uso non sempre razionale delle risorse. Emerge così l’esigenza di un rinnovamento dei sistemi di monitoraggio, che unisca il trasferimento tecnologico alle conoscenze scientifiche pregresse, verso usi mirati e calibrati sull'ambito viticolo, attraverso i quali poter attuare strategie previsionali che permettano la salvaguardia degli equilibri ecologici pur mantenendo inalterato il livello di produttività e qualità. Nello scenario della moderna viticoltura, il flusso di dati estratti dal campo proviene da fonti diverse tra loro. Si tratta di informazioni relative a diversi aspetti, che vanno dalla caratterizzazione della fisiologia delle piante, alla natura del contesto pedoclimatico fino a dati relativi alla gestione colturale: concimazione, irrigazione, potatura. Appare chiaro che, oltre a fornire grandi opportunità di indagine del sistema vigneto, questa abbondanza e diversificazione dei dati pone di fronte l’onere di dover gestire moli di dati spesso non strutturati che, pur avendo un grande valore intrinseco, richiedono di essere analizzate e sintetizzate affinché possano essere utilizzate in maniera proficua per la gestione agronomica del vigneto. Questi, infatti, se slegati dal contesto o se letti individualmente, danno spesso informazioni assai scarse, difficilmente leggibili, poco legate alla realtà applicativa e che in alcuni casi portano ad errori. Lo scopo dell’analisi di tali dati (chiamati non a caso Big Data) è quindi quello di individuare correlazioni, tendenze, pattern che si ripetono secondo schemi più o meno intuitivi, dinamiche di interdipendenza nascoste o comunque non facilmente identificabili, al fine di elaborare modelli simulativi costantemente aggiornati sulla base della biodiversità del panorama viticolo e dei contesti pedoclimatici, che consentano decisioni basate su dati più strettamente connessi alla realtà di campo anziché sulla semplice speculazione empirica o su serie storiche, con relativi vantaggi gestionali. Gli obiettivi della tesio sono stati quelli di: (i) sviluppare metodologie per l'acquisizione e l'analisi di immagini RGB dal contesto vigneto ed estrarre e analizzare i dati ad esse relativi per meglio comprendere le criticità, i vantaggi e le prospettive applicative di tale tecnologia; (ii) sviluppare modelli per la stima dello stato idrico della vite basati sull'analisi spazio-temporale di dati relativi al sistema pianta-suolo-atmosfera, per acquisire utili informazioni sulla gestione dell'irrigazione; (iii) applicare le metodologie e i modelli di simulazione sviluppati su casi studio reali per valutarne le prestazioni, confrontandoli con metodi esistenti, e analizzando la loro accuratezza nel fornire informazioni per la gestione sostenibile del vigneto

Viticulture under climate change: a case study on a water scarcity model

Changes in climatic patterns hinder the prediction of water availability, being imperative to develop new strategies to optimise water management in the agricultural sector. A multi-sensor network is being developed by ADVID/CoLAB VINES&WINES and University of Trás-os-Montes and Alto Douro (UTAD), aiming to determine water stress in vineyards, as a Decision Support System (DSS) for winegrowers. Remote wireless data transmission through LoRaWAN technology, will allow the development of a Machine Learning based model for water stress mapping. Measured parameters include soil, plant, and atmosphere data, given the importance of soil-plant-atmosphere continnum when evaluating water status. The pilot is installed in a commercial vineyard in the Douro Demarcated Region (DDR), and different sensor's modules were distributed spatially in the parcel. Lower cost and higher range than WiFi or Bluetooth, LoRaWAN are especially important for applications in remote areas, where mobile networks have little coverage, allowing to benefit a larger number of producers. While overcoming the constraints of the current monitoring method (Scholander pressure bomb), this system will allow remote and continuous water monitoring, assisting the producer in decision making. Altogether, this solution will contribute to better management of water resources, as well to the sustainability and competitiveness of farms.info:eu-repo/semantics/publishedVersio

Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping

Acknowledgments We thank Johan Havelaar, Aeryon Labs Inc., AeronVironment Inc. and Aeronautics Inc. for kindly permitting the use of materials in Fig. 1.Peer reviewedPublisher PD

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

This work was supported by the projects: "VIRTUOUS" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181, "SUSTAINABLE" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702 and the "Project of Excellence" from Junta de Andalucia 2020. Ref. P18-H0-4700. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.European Commission 101007702 872181Junta de Andalucia P18-H0-470