AgTech, Drainage, Precision Agriculture

UAV Regulations To Know

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Guidance for Operating Your Drone Safely

Simon Morris

Simon Morris

Altus UAS

 

 

Most people will have heard about UAV’s or drones (officially RPAS) and many know of the existence of regulations here in New Zealand, but few know exactly what the rules are and who they apply to.

Two websites offer particularly helpful information for users of UAVs or RPAS:

www.caa.govt.nz/rpas/

This is the dedicated webpage of Civil Aviation, the controlling authority. Information about Parts to Civil Aviation Rules that relate directly to RPAS are:

  • Part 101 Gyrogliders and Parasails, Unmanned Aircraft (including Balloons), Kites, and Rockets – Operating Rules, and
  • Part 102 Unmanned Aircraft Operator Certification.

Operators of RPAS also need to be aware of other rules that affect them, for example Part 91 General Operating and Flight Rules.

www.airshare.co.nz

Airshare acts as a UAV hub for New Zealand. It has information including how to operate your drone safely, plan all your UAV flights, and request access to controlled airspace.

You can find maps on the site showing where you can and cannot fly your UAV

NOTE   The information contained on Airshare is not to be relied on as a substitute for a comprehensive knowledge of the relevant rules and regulations that apply to the operation of UAVs. It is the UAV operator’s responsibility to read, understand and operate any UAVs in accordance with the Civil Aviation Rules.

AgTech

The Move to “Precision Forestry”

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Increasing the Resolution in which we Manage

david herries

David Herries
Interpine Innovation

 

 

Several industries recognise the benefits of increasing the resolution in which we manage.   We often refer to this as increasing the precision of our management thought smart decision making with better and more timely information; or “precision forestry”.

Considerable work is being done relating to remote sensing our forests with LiDAR, the use of UAV (a.k.a Drones or RPAS) technology in the sector, and adoption of computer based harvesting systems as the industry mechanises to reduce human health and safety risk.

David provided  an insight on the use of these technologies and their application in the forest sector, in the move to “precision forestry”.    This will reflect and provide a view on how other primary industry sectors are using technology to make gains in productivity and remain competitive on the international market.

AgTech, Plant Health, Research, Sensing

Vision System for Onion Crops

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Effective Sensing for Robotic Tasks- Still a Challenge

Chee Kit Wong

Kit Wong
Callaghan Innovation

 

Effective and reliable sensing for the performance of robotic tasks, such as manipulation in the outdoor environment remains a challenging problem.

While commercially available solutions such as ASA-LIFT are available for specific tasks and crops, and for operation in specific conditions, the systems are either not cost effective and or physically unsuitable for specific farming conditions and practices.

This research proposed to develop a mobile robot system with flexibility to adapt and with intelligence to cope with natural variability; through a two-fold aim utilising vision for navigation and manipulation. This talk discussed some of the recent developments on these aspects.

In particular, the talk focused on a novel approach that analyses point cloud information from a time-of-flight (ToF) camera to identify the location of foremost spring onions along the crop bed, for the intention of robotic manipulation. The process uses a combination of 2D image processing on the amplitude data, as well as 3D spatial analysis, extracted from the camera to locate the desired object.

Whilst the experimental results demonstrated the robustness of this approach, further testing was required to determine the ability of a system to cope with different scenarios that exist in the naturally varying environment.

For validation, the vision system was integrated with a robotic manipulation system and initial results of the investigation were presented.

AgTech, Automation, Precision Agriculture

Digital Agriculture – Challenges and Risks

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Associated Technologies Including Robotics

TristanPerez1

 

Tristan Perez, Queensland University of Technology

 

There is big hype at the moment about big data, big-data analytics, machine learning, artificial intelligence, and robotics. Some of these terms are starting to make it into agriculture, especially when we consider the potential impact of data flows from an integrated value chain.

There is little doubt that the judicious application of some of these concepts and associated technologies will be transformational to the agricultural industry. However, there are also some risks.

In this talk, I  attempted to define some of the terms above using simple examples within the agricultural context and discuss how the associated technologies including robotics could be applied.

I  also highlighted the challenges and risks associated with generating and using data without appropriate regard for the underlying management problems we seek to address.

AgTech, Automation, Planting, Research

Weeding Robots: A Global Review

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Current Prototypes of Field Robots

Armin Werner1 Rory Roten1 Luc van Rijen2
1 Lincoln Agritech PO Box 69 133, Lincoln 7640, New Zealand
2 HAS University of Applied Sciences, The Netherlands
Email: Armin.Werner@lincolnagritech.co.nz

Excitement is being generated at conferences, farmer’s workshops and the general media about the next level in precision agriculture: field robots. We all know that robots work in factories to carry parts or conduct specific actions in manufacturing and that indoor robots are already standard technology in many industries.

There is also interest in developing autonomous equivalents in agriculture beyond the current tractor-carried or self-propelled equipment. There are already some tomato and cucumber picking robots in use in greenhouses in Europe and North America. However, stepping out of the ‘controlled’ environment of a factory building or a greenhouse into the unforgiving conditions of an arable field, a pasture or an orchard poses many serious challenges for scientists and engineers.

Most of these technical challenges have been solved or at least solutions are prototyped for outdoor conditions. This includes the exact positioning and the orientation of the robot under changing ambient light and weather or infrastructure, the recognition of the dynamically changing environment of a field robot as well as precisely controlling the actions that a robot has to conduct in a highly variable environment.

With these preconditions in mind, it is most likely that agricultural robotics will develop very fast in the next years. It is unclear what the first major applications may be and what challenges farmers will face when it comes to using such robots as standard equipment. From discussions with farmers, growers and the industry it is anticipated that weeding robots for row crops (annual field crops, perennial tree and vine crops) will be a good candidate to pave the way for field robots into agriculture.

The presentation will give an overview of current prototypes and weeding robots which are commercially available. We will discuss general differences between these robots and the type of applications that are intended.

Automation, Drainage, Research, Sensing

A Digital Horticulture Research Strategy

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Value Chain Approach To Identifying Priorities

Roger Williams

Roger Williams
New Zealand Institute for Plant & Food Research Limited (PFR)

 

The industrial revolution gave us machines and agri-inputs that enabled us to farm at scale and speed. The green revolution began to unlock the potential of plant genes to increase yield. Now the digital revolution provides us with an opportunity to harness the power of ‘big data’ and technological innovation to radically re-engineer our horticultural production methods and supply chains.

Digitally informed decisions during production, harvesting, sorting, packing, storage and transit could be the basis for a step change to high profitability, high resource efficiency and low footprint horticultural value chains.

Identifying the research priorities that we need to realise this opportunity in New Zealand is a challenge in itself, given the pace of developments in sensing technology, robotics and the internet of things globally. Accordingly, Plant & Food Research assembled an expert panel from across its science teams, augmented with other specialists from New Zealand and Australia, to develop a digital horticulture research strategy.

The panel has taken a value chain approach to identifying research priorities, particularly in relation to production, harvesting, sorting and packaging, storage and transit.  Future science needs are structured around the concepts of ‘sense, think, act’ for each part of the value chain and are linked by an ‘artery’ of data to feed forwards and backwards along the value chain.

Plant & Food Research looks forward to working with a wide range of partners to deliver this digital horticulture strategy for the benefit of New Zealand’s producers and exporters.

AgTech, Research

The Value of Hyperspectral Data

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Big Data Technology

in Agricultural Research

Megan_CushnahanMegan Z Cushnahan – PhD Candidate New Zealand Centre for Precision Agriculture, Institute of Agriculture and Environment, Massey University, Palmerston North, New Zealand.

 

The use of big data technology is becoming more common in agricultural research. These emerging technologies, such as hyperspectral sensing and imaging (HIS) tools proffer to replace the expensive, laborious and time-consuming data-collection methods conventionally used in agriculture.

New approaches involve non-invasive, rapid collection of high volume, versatile data at a very high spectral and spatial resolution. Experience from other industries suggests however, that refining value from big data technologies will be a key challenge for the agricultural sector.

In order to understand the challenges and opportunities created by what we call a new data economy, the author is following a precision agriculture science team tasked with developing highly advanced hyperspectral techniques for a ‘low tech’ sector.

HSI creates multi-layered, geo-referenced data early in the science process in superabundance.  This data is created at high speed in real time and does not require expensive ground sampling.  The data is extremely versatile and has the potential for many different measurements from one record.  Early observations from the study indicate that these data traits may increase the likelihood of producing ‘surplus science’, that is, science that exceeds what was judged necessary to solve the problem as defined at project launch.

The production of superabundant and highly versatile data early in the science process appears to increase the possibility of discovering new forms of valuable knowledge (methods and solutions) during the course of an investigation. However, realizing the value of these opportunities may require a departure from the classic science model.

Under data-scarcity conditions, such surplus science would be classified as undesirable ‘project creep’. In response, we propose an alternative process based on a non-linear, iterative approach that utilises heterogeneous actors to refine value from hyperspectral data.

In addition, it is proposed that for innovation in the PA sector to make the necessary rapid advances both technically and in terms of adoption, changes are needed in the way research projects are funded and structured to accommodate a new approach to science-making.

Research

What’s “Big Data” all about?

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Moving from “what happened” analytics to “what will happen”

James BeechJames Beech, Senior Data Scientist BNZ/Brera Consulting Group

 

 

What’s “Big Data” all about ? This presentation will look at developments in the financial services as examples of what is coming for agriculture.

Financial services are aligning data sources to create single customer views. This requires a large overhaul of traditional systems, to build platforms to handle vast amount of disparate data (Variety, Velocity, Volume, Veracity). It offers greater understanding of customer needs so services can align the right product to the right customer using predictive modelling.

Financial services biggest change is moving from “what happened” analytics to “what will happen”.  They are using new techniques to understand causes and predictions, but to do so are building teams of data scientists and data story tellers.

Geospatial data sources and government data are enabling us to get a holistic customer view. Geospatial location of the customer base gives opportunities to align to market potential. Further, the Open Data approach by local government and NZ government reflect a new view of data and information as key public assets. Government believes making data available will drive innovation through better decision making and the creation of new services, tools, and knowledge.

An example is the ANZ Truckometer. After carefully analysing the data ANZ selected key routes and applied statistical techniques to smooth out anomalies and gaps. The result is a strong correlation between traffic flows and predicting economic growth or decline as measured by GDP data from Statistics New Zealand.

Another big data example involves Cancer identification in eye images through the application of Deep learning and Machine Learning. A predictive model was trained on 80,000 labelled images and shown to predict with 87% accuracy.  These algorithm parameters allow the ability to apply to other cyclical images.

An agricultural application would be an action oriented Agricultural Dashboard.  To get results, developers must focus on the problem being solved, not the product. Other lessons show delivery in a timely and relevant manner is crucial, it must build solid ROI cases and that the user interfaces and user experience are very important.

Focus less on “all the data” or the “perfect algorithm”. Use a short agile framework to roll out product and services. Customers will provide you the feedback for next iteration.

Farmers need end to end solutions, not part of the problem solved.

AgTech

Information Exchange Within the Value Chain

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Fresh produce chain –  from a supply to value chain orientation

Alistair MowattAlistair Mowat

Innovation and Strategic Management Consultant 

 

Retail strategies, food safety requirements, rationalisation of supply and innovation have all been significant factors in the ongoing transformation of fresh produce chains.

Strategically, retailers have been exerting greater control over product specifications in order to create a clear point of difference for the consumer. This control over specifications has also been important in managing variation in product supply.

Through these drivers, fresh produce chains have been evolving from a supply to a value chain orientation. This has required the building of trust between chain partners and the facilitation of information flows between these partners.

The greater the orientation, the more integrated partners become in the sharing of information. Chain partners become aligned in the creation and delivery of value that can benefit end-users.  At the same time these partners are identifying and removing costs in a way that does not erode value.

Within fresh produce chains technology is transforming how data can be captured to generate the information needed to optimise these chains. For example, historically, a disconnect has often existed between the comprehensive sharing of information between pre-harvest and post-harvest operations.

In some seasons, this disconnect can result in high storage losses. Precision farming technologies are now being used to better define and manage orchard variability. This information is being shared with the post-harvest sector to optimise the storage conditions of fresh produce on the basis of pre-harvest crop development.        

As technologies advance, we will see greater amounts of information being exchanged between the partners within a value chain to enable the whole chain to be more transparent and responsive to changing conditions. 

This value chain transparency can also create additional benefits for consumers who are seeking out fresh produce grown and handled with integrity and in an ethical way.    

AgTech, MicroFarm, Plant Health, Precision Agriculture, Projects, Sensing

Mapping Onion Canopies

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Investigating Technologies to Map Onion Crop Development

DanBloomer200

 

Dan Bloomer and Justin Pishief – Centre for Land and Water

 

The OnionsNZ/SFF Project “Benchmarking Variability in Onion Crops” is investigating technologies to map onion crop development. The purpose is to better understand variability and to gather information to inform tactical and strategic decision making.

An AgriOptics survey provided a Soil EM map of the MicroFarm which was used as a base data layer and helped select positions for Plant & Food’s research plots.

As the crop developed, repeated canopy surveys used a GreenSeeker NDVI sensor and CoverMap, a Smartphone application. Both were mounted side by side on a tractor fitted with sub-metre accuracy GPS.  Altus UAS provided UAV survey data including MicaSense imagery with five colour bands captured. A mid-season 0.5 m pixel NDVI satellite image was captured.

Both ground based systems had difficulty recording very small plants. GreenSeeker data were dominated by soil effects until a significant canopy was present. Once plants could be seen in photographs, the CoverMap system was able to distinguish between plants and soil.

Direct photos of Plant & Food plots were processed to calculate apparent ground cover. A very strong relationship was found between these and actual plant measurements of fresh leaf weight and leaf area index – both strongly correlated to final crop size.

Attempts to directly correlate the map layers with Plant & Food field plot measurements were frustrated by inadequate or inaccurate image location. Onion crops have been found highly variable over small distances. The GreenSeeker only records a reading every four or five metres, and CoverMap about every 1.5 m. Compounded by errors of a metre or more, finding a measurement to match a 0.5 m bed plot was not possible. Similarly, the UAV and satellite images, while able to identify plots, did not initially show correlations.

Using ArcGIS, fishnets were constructed over the various canopy data layers and correlations between them found at 5 m and 10 m grids. The 10 m grid appears to collect enough data points even for the GreenSeeker to provide a reasonable if not strong correlation with other canopy layers.  Similar processes are being used to compare soil and canopy data.

After one season of capture, there appears to be merit in using an optical canopy cover assessment as plants develop. Once full canopy is achieved, the NDVI or a similar index may be better. Colour image analysis will be tested as a method of recording crop top-down as a measure of maturity and storage potential.

We were not successful in mapping yield directly, but did identify a process for creating a yield map based on earlier crop canopy data.

Promoting sustainable production