IoT-ready Sensor Technology for Connected Farms

Transcription

1 IoT-ready Sensor Technology for Connected Farms Krikor Ozanyan and Bruce Grieve School of Electrical and Electronic Engineering UK>

2 Sensors in 1 st generation IoT First wave: All Data Are Sensed Data acquisition COTS sensors for basic measurands miniaturisation low consumption circuits energy harvesting wireless-connected Data processing Basic sensor fusion tripod Architectures Algorithms A Applications A A

3 Livestock farms - the supply chain Managing the chain - essential for efficiency and resilience Upstream (e.g. food) ingredients processing and storage formulation transport Downstream (e.g. abattoir) transport schedule numbers/weight Farm Food Abattoir

4 Connected Farms in context: Improve food conversion Food intake is accountable by volume/weight Animal mass can be monitored by weight Food intake is regular Animal weighing is much more challenging to manage regularly and accurately Existing weighing technology is either expensive and/or o requires substantial maintenance; o requires staff engagement; o depends on manual/visual handling; o results in low throughput; o delivers raw data only o is difficult to connect

5 ? C OTS methodology needed Weighing - mature and widely spread, well commercialised For connected pig farms we need (at minimum overhead) Inexpensive, multiple weighing sites (100% covered) Allows regular weighing, as part of the farm routine Easy integration of animal ID (tags or camera) Unobtrusive for the animals Fully connected, upgradeable, re-programmable Low farm-floor maintenance, minimal disruption

6 Borrowing imagimat technology IEEE Sensors J. 15 (1) (2015) doi: /JSEN New tomography type sensing, originally developed for human gait Based on deformation of plastic optical fibres, scales well in area Materials cost: ~ 100 m -2 Translation to livestock farms: Deployment in hostile environments materials sandwich to allow reliable encapsulation without compromising sensitivity Connectivity and flexibility (upgrading/re-programming) Several application scenarios, based on sensing floor deformation

7 Borrowing imagimat technology Data acquisition imagimat is based on collaborative sensor fusion from multiple POF sensors arranged as a carefully designed grid in a single plane No wires and no electrical currents under the mat, electronics at the periphery Data processing Imaging is by solving an inverse problem special methods developed for reconstruction at severe undersampling e.g. Hough transform. Real-time reconstruction is speeded up by parallel computation in embedded multiprocessor systems / FPGAs Automatic decision making without visualisation classifications by artificial intelligence (deep neural networks) Resources and communications efficiency is achieved by balancing on-board and hub processing, as well as balancing real-time vs post-processing.

8 Agri-Food IoT, e.g. Above Crop Below Soil and beyond the Farm Gate Sensors for protecting crop YIELD A sensor-network of 24/7 in-field disease pressure monitors Sensors trick pathogen into germination on mimic surface 2.5M leveraged funding, TSB Crop Protection competition Sub-Soil Imaging New visualisation tool for seeds breeders Tracks how efficiently the root bundles are in drawing upon the water and nutrients in the soil. An in-field tool for early isolation and delivery of tomorrow's climate tolerant food crops Plastic Electronic-sensor Tagging (PET) UK, 4.1M tonnes of food that could have been eaten is thrown away A technology platform for recording produce temperature stress profiles from low cost, battery-free, plastic RFIDs 1.5M leveraged funding from TSB Plastic Electronics competition

9 Multiuse Networked Platforms, e.g. IoT Crop Tricorder for < US$100 Image courtesy of CBS Inc. Crop disease sensing Early weed / plant detection; Speciating insect disease-vectors; Carbon footprint (protein in crops); Leaf viral symptoms; Connected GPS, camera, microprocessor + Subtle light manipulation = Active Close-Proximity Hyperspectral Imaging Fluorescence Imaging Stereo photometric Imaging Solid State Active Ellipsometry Harvested fruit bruising & sugars Soil organic carbon Aquatic parasites Frogs in salads... N8 AgriFood One Network, Many Solutions

10 The near horizon Cloud balance between data and information Big Data management, IoT+ (C)OTS start bolt-on IoT capability necessary Standards for modular structures where applicable People education and training compatible with the spread and evolution of IoT