Category Archives: AgTech

Agronomy, AgTech, Automation, Irrigation, MicroFarm, Nutrients, Plant Health, Precision Agriculture, Projects, Research, Sensing, Soil

Onion Crop Research Plan

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After identifying areas within paddocks that had yields limited by different probably causes, we conceived the idea of Management Action Zones (MAZs).

Yield assessments show considerable variation, limits imposed by population, growth of individual plants, or both
Yield assessments show considerable variation, limits imposed by population, growth of individual plants, or both

Some areas showed that yield was limited by plant number: establishment was poor. Others had the expected population, but low biomass: the plants were small due to some other limiting factor.

If we can identify zones easily, and determine the causes, we should be able to target a management response accordingly. So for this season, we set out a revised research aim.

What we want to know:

  • Can we successfully determine a management action zone in a field?

Why do we need to know this?

  • Develop a tool to increase uniformity and yield outcomes
  • Develop a tool to evaluate management practices and crop productivity

If we want to successfully determine a management action zone in a field then there are two main steps to achieve in this year’s work:

  • Confirm the relationship between digital data and crop model parameters
    • Does the relationship stay constant over time and sites?
    • How early in growth can a difference be detected?
    • Can the relationship be used to show a growth map across a field?
  • Develop an approach to gather information and ways to input and display results, initially using a website approach.
    • Can we integrate a plant count and yield information to start developing a management action zone?
    • How should this be put together in a way growers can start to use to gather information about their crops?

At the MicroFarm, we established six research zones based on paddock history and excessive wetness at establishment.

We have three paddock histories: two years of onion production with autumn cover crops of Caliente mustard, two years of onion production with autumn cover crops of oats, and no previous onion crops planted after previous summer sweetcorn and autumn sown rye grass. In each of these areas, we deliberately created sub-zones  by applying about 45mm of spray irrigation as a “large rain event”.

Artificial heavy rain event applied after planting and before emergence
Artificial heavy rain event applied after planting and before emergence

The impact of the artificial rainstorm is evident on images taken at the end of November.

The lasting effect of a heavy (artificial) rain event pre-emergence (right panel) shows low population and poor growth compared to areas without heavy rain (left panel)
The lasting effect of a heavy (artificial) rain event pre-emergence (right panel) shows low population and poor growth compared to areas without heavy rain (left panel)
AgTech, Automation, Irrigation, Nutrients, Precision Agriculture, Sensing, Soil, Water

Technology to Reduce N Leaching

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N-Leach_WorkshopThe Precision Agriculture Association NZ is presenting workshops focused on technologies available to help reduce nitrogen leaching. There are two North Island workshops being offered at:

Massey University on Thursday 1st September 2016 [PDF here]

and

Ellwood Centre, Hastings on Friday 2nd September 2016 [PDF here]

Programme

The ‘Technology to Reduce N Leaching’ workshops are similar to the well received program conducted in Ashburton in March 2016 and will address where we are and what we can do about nitrate leaching limits in a North Island context utilising a range of technologies and farm systems options.

The particular areas for focus for the program are:

  • Variable rate technologies and systems
  • Precision irrigation
  • Precision spreading systems and services
  • Soil mapping
  • Soil moisture monitoring, sensors, metering
  • Nutrient budgeting and environmental monitoring

A Q & A time slot is devoted in the afternoon session for attendees to interact with members and presenters on the day to share learnings and understandings about the issues. This will also be possible over the lunch break on both days with one and half hours devoted for this.

PAANZ2

Offer to PAANZ Members

As part of the Hastings program only on 2nd September, PAANZ members are offered the opportunity to participate as trade/sector participants for technologies and products as may be appropriate to support the program.

PAANZ is not able to offer trade/sector stand space at the Palmerston North venue due to space restrictions unfortunately so only the Hastings venue will be able to accommodate this option for members.

If you would like to participate please advise Jim Grennell, E-mail: jim@paanz.co.nz

Mobile: 021 330 626, places are limited to ten organisations for the Hastings workshop to be involved as a trade/sector participant so it will be on a first come basis.

The cost of participation will be $100.00 plus GST per stand with attendance fee of $100.00 per person additional.

As these are indoors Workshops, with a technology focus and space at the Hastings venue is limited no large equipment or hardware can be accommodated.

Confirmation of members wishing to take up this opportunity is required by Monday 22nd August 2016 after which time the opportunity to participate will be made available to non-members.

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.

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.

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.