Smart Farming: Opportuni0es, Challenges and Technology Enablers
Manlio Bacco1, A. Berton2, E. Ferro1, C. Gennaro1, A. Go2a1, S. Ma2eoli3, F. Paonessa3, M. Ruggeri4, G. Virone3, A. Zanella3
1 Ins0tute of Informa0on Science and Technologies (ISTI)
2 Inst. of Clinical Physiology (IFC) 3 Inst. of Electronics, Computer and TelecommunicaKon Engineering (IEIIT)
4 Inst. of Agricultural MechanizaKon, Earth-‐Moving Machinery and Off-‐Road Vehicles (IMAMOTER) Na0onal Research Council (CNR), Italy
ireless Networks Laboratory, ISTI CNR – NaK
onal Research Co
uncil, Pisa
Outline
• Internet of Things for agricultural applicaKons – relevant use cases – enabling wireless technologies – network funcKonal requirements
• a quick glance at: – Unmanned Aerial Systems (UAS) – Machine Vision and Deep Learning
• conclusions
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M2M / IoT -‐ a brief digression -‐
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machine-‐to-‐machine: • refers to technologies that
allow both wireless and wired systems to communicate with other devices of the same ability
• no direct human consumer or producer
Internet of Things: from custom communicaKon soluKons to a common stack
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• large diversity of applicaKon domains and of communicaKon requirements
• heterogeneity and fragmentaKon of the connecKvity landscape thet is currently hampering the full realizaKon of the IoT vision, by posing several complex integraKon challenges
In fact, there is not a true IoT ecosystem, but a plethora of verKcal soluKons that are not interconnected (as IoT would suggest)
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M2M / IoT -‐ a brief digression -‐
IoT is not a single technology, but it is a technology enabler
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M2M / IoT -‐ interoperability and compaKbility -‐
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INTEROPERABILITY is the ability of two or more systems to exchange and use informaKon: the informaKon reaches the target system and sKll mean the same thing to the target system as the sending system.
COMPATIBILITY is the ability of two or more systems to perform their required funcKons while sharing the same environment. The two systems do not need to communicated with each other, but simply be resident on the same environment.
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Agricultural Electronic FoundaKon deeply involved in precision agriculture, enabled by ISOBUS (ISO 11783) “Tractors and machinery for agriculture and forestry -‐ Serial control and communica8ons data network” Time-‐Sensi0ve Networking (TSN) capabili0es for industrial automa0on (an addi0on to the Ethernet standard) represent a further step forward into suppor0ng high speed communica0ons from/to agricultural machines. The evoluKon of ISOBUS by relying on TSN features is based on the exploitaKon of the BroadR-‐Reach standard (from the automo8ve sector), a physical layer operaKng full duplex over one pair of unshielded twisted pair cables.
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ISOBUS (ISO 11783)
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ISOBUS can be considered a mature standard, but the same cannot be said for the standards for wireless in-‐field communica0on in smart farming scenarios. Several reasons are behind that: • the vast ecosystem of wireless standards • the vast ecosystem of standards for automated funcKons • commercial interests
Because of this, a number of wireless standards are expected to be included into ISO/TC 23/SC 4 “Tractors and machinery for agriculture and forestry”
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wireless communica0ons
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Farm Management Informa0on Systems using the informaKon coming from various sources as sensors to assist farmers in the decision-‐making process • soil samples • sensor data • aerial devices • weather informaKon • maps • …
Extended FMIS Data Interface (EFDI) machine-‐to-‐cloud or cloud-‐to-‐cloud standard based on ISO 11783 focusing on protocols for data transfers using wireless communicaKons by exploiKng ISOXML
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background and relevant use cases
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Extensive cul0va0ons -‐ long-‐range communicaKons -‐
fixed wing UAV
n-‐rotors UAV
large distances (in the order of kms)
clusters of huge machines working in parallel • real-‐Kme
communicaKons? • among machines √ • with the CC ?
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Orchards and vineyards -‐ medium and short-‐range communicaKons -‐
rows of plants precisely posi0oned for automated and specialized machines • large use of WSNs • role of UAVs?
n-‐rotors UAV
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command and control data • typically small amounts of data, but with strict constraints on real-‐Kme and
on reliability • is it sKll so for large clusters of cooperaKng devices? (speed and mulKple
access)
payload(s)-‐generated data • possibly large amount of data with less stricter constraints • very high-‐speed communicaKons for large clusters of cooperaKng devices
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ground and aerial vehicles -‐ data types -‐
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IEEE 802.15.4 (telemetry): • low data-‐rate • reduced coverage Bluetooth 4.2 / 5 (telemetry): • IPv6 / 6LowPAN (IP
addressable!) • reduced coverage
LoRA / LoRAWAN (telemetry): • low data-‐rate • long-‐range
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wireless technologies 802.11 (large data exchanges): • b/g/n/ac • p: vehicular standard
– max 1W of transmit power!
3G/4G/5G (large data exchanges): • coverage? • economical feasibility?
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(slow) agricultural machines (in public roads: ad-‐hoc adver0sement?) 802.11p / DSRC • carrier sense mulKple access with collision avoidance medium-‐access scheme • some challenges when guaranteeing strict reliability levels and ensuring the
network’s scalability (as the load increases) LTE-‐V (sidelink 5G V2X vehicular communica0ons) • improves the link budget w.r.t. 802.11p • redundant transmissions per packet • mode 4 does not require cellular coverage!
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wireless technologies
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aerial vehicles
high resolu0on details and on-‐demand imagery • water stress level • RVI, NDVI, PRI (vegetaKon indices) • weed detecKon • (autonomous) spraying (VRT) • …
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aerial vehicles by CNR for smart farming
EFESTO (UAV) by Sigma Ingegneria, CNR Pisa and Zefiro • pest control • imagery (visible spectral bands and/or in those outside the visible range)
Security regula0ons for flying objects are evolving day by day, even if rural areas suffer from less constraints than urban areas
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aerial vehicles by CNR for smart farming
visible range im.
multispectral im.
thermal im.
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a simple taxonomy for UAV missions
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MV-‐based applica0ons for (hyperspectral) imagery from an UAV: • agriculture parameter esKmaKon and retrieval (e.g. carotenoid, chlorophyll,
and anthocyanin content, nitrogen status, water content, biomass and leaf area index)
• crop classificaKon and crop/material detecKon • disease early detecKon and monitoring (e.g. disease due to bacteria and fungi
or caused by insect infestaKon) • machinery guidance in pre/post crop tasks (e.g. early sowing, precision
ferKlizer applicaKon, precision irrigaKon) • …
MV coupled with Deep Learning techniques enables intelligence and reconnaissance scenarios in different farming scenarios!
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Machine Vision
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The role of 5G in future smart farming scenarios
5G, along with aerospace soluKons (such as UAVs), may bring broadband connecKvity in rural areas, providing IoT scenarios with fast and reliable connec0ons soawariza0on • SDN/NFV • network slicing low-‐cost solu0ons • commodity hardware • on-‐demand deployments
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a future scenario?
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uncil, Pisa
IoT VerKcal and Topical Summit for Agriculture, 08-‐09/05/2018 Italy
conclusions • highlighted the importance of interoperability for IoT deployments • presented the wired and wireless networking soluKons in use at today
• highlighted how wireless networking sKll lacks a standard to ease interoperability
• discussed different requirements in long-‐range and short-‐range agricultural deployments
• role and requirements for UAV complemented by MV and DL • 5G and future scenarios
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Smart Farming: OpportuniKes, Challenges and Technology Enablers
Any ques0ons?
Thank you!
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