HG09044 MS105 Approved)

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Project HG09044

Review of Mechanisation, Automation,

Robotics and Remote Sensing (MARRS)

for Australian horticulture

Author - Russel Rankin

Food Innovation Partners Pty Ltd

Horticulture Australia Ltd

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HG09044 – Mechanisation, Automation, Robotics and Remote Sensing

(MARRS) in Australian horticulture

January, 2010

Project Leader: Russel Rankin, Food Innovation Partners Pty Ltd,

Contact Details: 1264 Mt Samson Rd, SAMSONVALE QLD 4520 Mobile. 0411178227. Email: [email protected] Internet: www.food-innovation.com.au

Purpose: This Literature review was prepared as an outcome of Milestone 105 of project

HG09044,” Scoping study to review Mechanisation, Automation, Robotics and Remote Sensing in Australian horticulture”. The Literature Review aims to provide a broad overview of MARRS -type technologies being applied to horticulture here and around the world. Case studies have been included to provide examples of the value and advantages of MARRS applications in horticulture, and of the issues that need consideration. Both pre-harvest and post-harvest applications have been included. Note that this is not intended to be a definitive report of MARRS applications in horticulture.

Acknowledgment of funding sources:

Food Innovation Partners acknowledge the financial support for this project from Horticulture Australia Limited (HAL).

Disclaimers: Any recommendations contained in this publication do not necessarily represent current HAL Limited policy. No person should act on the basis of the contents of this publication, whether as to matters of fact or opinion or other content, without first obtaining specific,

independent professional advice in respect of the matters set out in this publication.

While every effort has been made to ensure the accuracy of information contained in this report Food Innovation Partners are unable to make any warranties in relation to the information contained herein. Food Innovation Partners disclaims liability for any loss or damage that may arise as a consequence of any person relying on the information contained in this document.

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MEDIA SUMMARY

The objective of this project HG09044, “Scoping study to review Mechanisation, Automation, Robotics and Remote Sensing (MARRS) in Australian horticulture” is to provide a broad review of work being undertaken in developing MARRS technologies and solutions, and how these might address the drivers affecting the competitiveness of the industry.

As the extent of MARRS developments are vast, varied and the horticulture industry consists of many crops from lettuces and carrots through to grapes, apples and bananas, an overview approach was adopted to enable the project to gain a broad understanding of progress globally in relation to developments and potential barriers to successful commercialisation. In Australia the horticulture industry is made up of 47 separate sectors.

Case studies have been included to show the value and advantages MARRS applications can provide horticulture in Australia and to raise awareness of the issues that need consideration in the development and implementation of these types of solutions.

Both pre-harvest and post-harvest applications have been included. This review identified a number of critical factors that need to go hand-in-hand with the development of MARRS technologies in horticulture. They are;

1. Agronomy and growing systems that are designed for the effective and efficient application of a mechanisation, automation or robotic system. This is important, in particular for harvesting and crop management systems.

2. A clear path to commercialisation of the technology solution. This activity needs to also consider the business model that a firm will create to make the technology viable.

3. Maintenance and service infrastructure. The development of a supporting infrastructure is also crucial to successful deployment of MARRS solutions as the horticulture industry is located in rural and regional Australia where traditional skill levels in these regions are not focused on MARRS technologies although this is rapidly changing.

The development and application of remote sensing technologies is maturing and its implementation and usage increasing. Advances in the technologies and increases in their applications will continue. There are fewer challenges to the application of remote sensing technologies due to the fact that these types of the technologies are non-contact. This is not the case with development of automated harvesting, pruning and plant management systems. This report identified that for applications of MARRS technologies where plant contact is required such as harvesting; there are significant challenges to be overcome. For example the

development of robotic harvesting systems will require developments in agronomy in parallel. The elements of agronomy that in many cases will be critical in successful development and

implementation of automation solutions will be plant structure and size through both variety selection as well as modified growing structure. For example the development of robotic apple harvesting may require apples to be grown under a trellis system. The orientation of these trellis systems will also be important in terms of maximizing the sunlight exposure for plant growth and fruit ripening.

This report has also highlighted the critical importance of developing appropriate business models for successful commercialisation of any MARRS technology. The business model can be seen as the way in which the commercialiser of the technology will make money in the market place. Companies can create and capture value from their new technologies in three basic ways: through incorporating the technology in their current businesses, through licensing the technology to other firms or through launching new ventures that exploit the technology in new markets. Maintenance and service infrastructure is the third critical dimension to successful

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crucial to successful deployment of MARRS solutions, as the horticulture industry is located in rural and regional Australia and traditional skill levels in these regions are not based around MARRS technologies. Going forward, thought will need to be given to the development of this infrastructure through training and remote support processes.

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TECHNICAL SUMMARY

The objective of this Report for project HG09044, “Scoping study to review Mechanisation, Automation, Robotics and Remote Sensing (MARRS) in Australian horticulture” is to provide a broad review of work being undertaken in developing MARRS technologies and solutions, and how these might address the drivers affecting the competitiveness of the industry.

As the extent of MARRS developments are vast, varied and the horticulture industry consists of many crops from lettuces and carrots through to grapes, apples and bananas this approach was adopted to enable the project to gain a broad understanding of progress globally in relation to developments and potential barriers to successful commercialisation. In Australia the horticulture industry is made up of 47 separate sectors.

Case studies have been included to provide examples of the value and advantages MARRS applications provide horticulture in Australia and raise awareness of the issues that need consideration in the development and implementation of these types of solutions. These Case Studies also demonstrate some of the critical factors identified below.

Both pre-harvest and post-harvest applications have been included. This review identified a number of critical factors that need to go hand-in-hand with the development and introduction of MARRS technologies to horticulture. They are;

1. Agronomy and growing systems that are designed for the effective and efficient application of a mechanisation, automation or robotic system. This is important, in particular for harvesting and crop management systems.

2. A clear path to commercialisation of the technology solution. This activity needs to also consider the business model that a firm will create to make the operation viable.

3. Maintenance and service infrastructure. The development of a supporting infrastructure is also crucial to successful deployment of MARRS solutions as the horticulture industry is located in rural and regional Australia where traditional skill levels in these regions are not focused on MARRS technologies although this is rapidly changing.

The development and application of remote sensing technologies is maturing and its implementation and usage increasing. Advances in the technologies and increases in their applications will continue. There are fewer challenges to the application of remote sensing technologies due to the fact that these types of the technologies are non-contact. This is not the case with development of automated harvesting, pruning and plant management systems. This report identified that for applications of MARRS technologies where plant contact is required such as harvesting; there are significant challenges to be overcome. For example the

development of robotic harvesting systems will require developments in agronomy in parallel. The elements of agronomy that in many cases will be critical in successful development and

implementation of automation solutions will be plant structure and size through both variety selection as well as modified growing structure. For example the development of robotic apple harvesting may require apples to be grown under a trellis system. The orientation of these trellis systems will also be important in terms of maximizing the sunlight exposure for plant growth and fruit ripening.

This report has also highlighted the critical importance of developing appropriate business models for successful commercialisation of any MARRS technology. The business model can be seen as the way in which the commercialiser of the technology will make money in the market place.

Companies can create and capture value from their new technologies in three basic ways: through incorporating the technology in their current businesses, through licensing the technology to other firms or through launching new ventures that exploit the technology in new markets.

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The functions of a business model are as follows:

1. Articulate the value proposition (the value created for users by the offering based on the technology)

2. Identify market segments. Users to whom the technology is useful and the purpose for which it will be used.

3. Define the structure of the company’s value chain which is required to create and distribute the offering and determine the assets needed to support the firm’s position in this chain.

4. Specify the revenue generation mechanism for the company

5. Describe the position of the company within the value network, linking suppliers and customers

6. Formulate the competitive strategy by which the company will gain and hold over rivals. 7. Assess capability required to achieve commercialisation.

At a firm level, the critical issue will be the payback period on their investment and on-going maintenance: servicing and spare-parts related to MARRS technologies.

Maintenance and service infrastructure is the third critical dimension to successful

implementation of MARRS solutions in the future. The development of a support infrastructure is crucial to successful deployment of MARRS solutions, as the horticulture industry is located in rural and regional Australia and traditional skill levels in these regions are not based around MARRS technologies. Going forward, thought will need to be given to the development of this infrastructure through training and remote support processes.

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INTRODUCTION

The National Horticultural Research Network (NHRN) was established in 2001 and comprises the Horticultural R&D managers from the State Departments of Primary Industries, CSIRO and University of Tasmania. The focus of the NHRN is collaboration and strategic leadership for R&D to support viable horticulture industries in Australia. NHRN formally meets three times per year – primarily with Horticulture Australia Limited (HAL).

In 2008 the NHRN undertook a review of all the Horticulture Industry reports received from within its network for the review of prospects in “Mechanisation, Automation, Robotics, and Remote Sensing” (MARRS). The committee was of the opinion that there are a number of opportunities to introduce MARRS-related technologies and advances at all levels of Australia’s horticultural operations.1 However, the rate of success and the commercial viability of the possible solutions, vary to a great extent. From an engineering point of view, crop layout structure (eg.

glasshouses/greenhouses, highly defined field rows, intensive orchards etc) is the most

fundamental aspect for MARRS solutions to be applied most effectively to secure a commercial advantage. Despite structured crop layout, some crops do not lend themselves to bear fruit in a structured way. In such situations, major agronomical input is necessary in the area of plant research. As extreme examples, baby leaf and lettuce can be laid out in a very structured manner while avocado may not be able to be grown so as to present its fruit in a structured way that will facilitate automated harvesting.

MARRS-related opportunities can be broadly categorized into three areas – crop production, harvest and postharvest. In the case of crop production, crop yield monitors could use precision agriculture and crop sensor applications (remote sensors) allowing growers to provide more accurate water and fertiliser regimes critical in times of drought and high fertiliser costs. The grower would also be better informed to predict physiological events (eg. flowering, fruit set, pest incursions, maturity indices) enabling them to better manage spray regimes, worker schedules, and most importantly predict market yield for domestic and export markets. The technology and software associated with many of these applications is still very much in its infancy and would usually require the producer to be technology literate in order for them to obtain the greatest use from these systems.

For harvest operations, the degree of structure varies significantly across the types of crops, hence the success rate of MARRS uptake and application is varied. However, in the case of postharvest operations the structure remains significantly constant. Hence the prospects of MARRS usage in postharvest operations are much higher (certainly in the short-term) than those of harvest operations.

The main aim of undertaking MARRS research in horticulture is to achieve competitiveness in the Australian industry in relation to that of international markets. Therefore, to performance rates are of utmost importance. Bearing in mind that Australia currently competes with other emerging economies with significantly larger and ‘cheaper’ labour pool, the solutions proposed must be able to match the traditional manual production rates. In some cases OH&S issues may also need to be addressed.

Australia is well placed to achieve significant gains by taking up MARRS-related technologies, particularly in the crop production and harvest operations of structured crops. To achieve

commercial advantages in other crops, thorough investigations are necessary to reduce/eliminate or combine crop production, harvest and post harvest operations. As an example, a cucumber ‘harvester’ deployed in a protected plantation may be used to determine an individual plant’s nutrition or pest incursion level for directed fertilizer and pesticide application, the ripeness quality and size, determine whether to harvest or not, then during harvest conduct an instant fruit

inspection for blemishes and other defects, grade and package. The NHRN review indicated the possibilities of process integration to minimize costs and increase throughput so that a

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It is of utmost importance that Australia’s horticulture industries start to recognize MARRS

solutions as part of the entire process. Any MARRS assessment must be carried out on the entire process with and without automation to determine the commercial and economic advantages. Most often the assessment is carried out only on the part that is first considered for automation. It is also quite possible that MARRS solutions may introduce additional MARRS-associated

problems to be solved and hence the entire process may have been adversely affected with its introduction, so it is important to thoroughly assess a situation prior to investment. The Review undertaken by the NHRN also recognized that much could be learnt from other industries that have already embraced these strategies both here in Australia as well as overseas programs around automation in agriculture, in particular New Zealand.

The committee also noted that there are crop groups that lend themselves to MARRS solutions however; they more than likely do not have the financial strength to fund the development of MARRS solutions that may benefit them. Hence there is a need to assess all industries to ensure replication does not occur and that knowledge of new applications is shared amongst the whole of the horticulture industry as much as possible to reduce costs. In this way, smaller industries will likely benefit from technologies developed by larger industries.

In December 2008, at the meeting of the National Horticultural Research Network (NHRN), it was agreed that they would seek to commission through Horticulture Australia (HAL) a scoping study on the application of Mechanisation, Automation, Robotics and Remote Sensing (MARRS) technologies to horticulture in Australia.

This scoping study would also develop the Business Case for commercial, industry, State and Commonwealth investment (via HAL) in the development and application of MARRS technologies to Australian horticulture. The study was given the working title “Re-engineering horticulture: using MARRS technologies to radically improve the international competitiveness of Australian Horticulture”. A Terms of Reference for this scoping study was prepared and Project HG09044: Mechanisation, Automation, Robotics and Remote Sensing (MARRS) in Australian horticulture commissioned.

Australia’s horticulture sector

The horticulture sector is the second largest sector within Australian agriculture, being slightly less than the grains industry, but well above the combined average contributions of the wool and dairy industries2.

Horticulture is diverse incorporating 140 commodities; including industry such as vegetables, fruit, nuts, nursery, turf, cut flowers and extractive crops. Table and dried grapes, but not wine, are also part of the sector.

Horticulture is also geographically diverse – with horticultural commodities undertaken in almost all 56 catchment areas across Australia. The major growing areas for edible horticulture include the Goulburn Valley of Victoria; the Murrumbidgee Irrigation Area of New South Wales; the Sunraysia district of Victoria/New South Wales; the Riverland region of South Australia; northern Tasmania; southwest Western Australia and the coastal strip of both northern New South Wales and Queensland. Nursery and turf production generally occurs within or close to the capital cities and regional centres.

Banana, pineapple, mandarin, avocado, mango and fresh tomato production is concentrated in Queensland; stone fruit and oranges in New South Wales, Victoria and South Australia;

processing potatoes in Tasmania; fresh pears, canning fruit and processing tomatoes in Victoria; and apples and fresh vegetables in all States.

Productive capacity

The two largest product sectors of horticulture, fruit and vegetables have generally achieved increasing GVPs (Gross Value of Production) since 1999-2000. The fruit GVP increased every year apart from 2003-2004 which followed a severe drought. The vegetable GVP has been more

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variable and vulnerable to droughts. It has also experienced a significant market downturn for processing vegetables.

Since 2000-2001, the main constraint on the industry’s productive capacity has been climate variability and the impact of two severe droughts in quick succession on production and farm profitability. Low water availability from natural rainfall and restricted irrigation water allocations have been the key production-limiting factors.3

Drivers in Australia’s horticulture Industry

The key drivers effecting Australia’s horticulture industry in today’s economic environment have been widely recognized as:

• Competitiveness, labour shortages, • Green technologies and sustainability, • Industry location,

• Food safety and security, and • Consumer choice.

The increasing costs of production associated with a strong dependency on a secure labour force, greater scrutiny of food safety issues and consumer expectations for environmentally responsible production processes, are driving the industry to better understand, measure and strategically respond to issues involving mechanisation, automation, robotics and remote sensing capability.

Despite the challenges facing Australia’s horticulture industry it has experienced strong growth over the past decade. The inadequacies of national data on industry employment requirements and the absence of aggregated vacancy data mean that it is difficult to systematically document the labour shortage issues in rural Australia.4

The Queensland Fruit and Vegetable Growers Association (Growcom) estimates that due to labour shortages, during harvesting, its members lose up to 10 per cent of their crops – produce estimated to be worth $900 million. Such problems are not confined to Queensland. Fruit growers from around Bunbury in the southwest of Western Australia say demand for orchard workers outstrips supply, particularly during harvest season. In Victoria, SPC-Ardmona, says that for the last three years production at their Shepparton cannery has been lower than it might have been because fruit has been left on the trees as there aren’t enough people to pick it, while a Yarra Valley berry grower says labour shortages in 2004 forced him to ‘drop’ 6 tonnes of raspberries from his vines. A leading Australian fruit exporter says the lack of a reliable supply of seasonal labour significantly inhibits industry growth in the Murray Valley irrigation region and limits export income.5

The Australian Horticulture Industry cannot afford to take a fragmented approach to MARRS and so consistency of knowledge, interpretation, and application within the industry is vital as the industry comes to terms with a wide range of challenging global issues.

The Australian Horticulture industry is looking to have a thorough and up-to-date understanding of the MARRS capabilities within Australia (and overseas where applicable) and what it can offer to this industry. The NHRN, Future Focus process, and HAL Postharvest and Emerging

Technologies Portfolio have all identified the need for an across-industry approach to the development of MARRS capability and applications.

Many other agricultural industries and industry businesses are already down the path of developing these capabilities. However, there is a need to ensure consistency and reduce the risk of duplication in future funding. It is therefore critical that this study is developed and

accepted by the Horticulture Industry as a whole (which includes major Agribusinesses) and that it is capable of delivering meaningful outcomes for all stakeholders.

Mechanisation, automation, robotics and remote sensing in the horticulture industry are a high priority for the HAL Postharvest and Emerging Technologies Portfolio. There has been some

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significant investment in this area already, however HAL wants to ensure a consistency of approach and cost effectiveness of any future investment in this area as the industry moves forward. This report is the result of a review and assessment of existing technologies that been applied to horticulture both here and overseas.

Robotics and automation

Robotics and automation in Agriculture is not a new phenomenon: In controlled environments it has a history of over 20 years. However, with the latest increase in computational power

combined with a cost reduction, robotics applications are spreading. The development of mechanical assistance or automation in harvesting systems began as early 1883 when Hugh Victor McKay, a 17 year old, tired of turning the heavy handle on his fathers’ winnowing machine in country Victoria, Australia, wondered if a harvester could be made to winnow as well. With the help of his brothers George and John, he built a prototype made of old metal scraps and farm tools.

It was finished in 1884 and called the Sunshine Harvester. It was an immediate success because it separated the grain, straw and chaff using a rotary fan making the entire harvesting process automatic.

The study of robot applications for plant production also had an early start in 1984 with a tomato harvesting robot6. Currently there are automated harvesters in the research phase for cherry tomatoes, cucumber, mushrooms, cherry and other fruits. In horticulture, robots have been applied to harvest citrus and apples. So far, no harvesting robot has reached the stage of commercialization, because of their low operation speeds, low success rates, and high costs. The key areas associated with the application of automation to horticultural that have significant challenges to be addressed are:

• Path finding; navigation both within the rows of an crops and orchards and in order to get to the field,

• Mapping; keeping track of where the robotic task has already been completed and where it remains to be done,

• Vision; computer vision recognition of the target such as the trunk, the fruit/produce, the bud, the flower, etc,

• The design of the mechanical system or robot which will perform the task of picking, spraying, pruning, pollinating, etc,

• Building an automation platform which is cost effective, can handle rough terrain, sloping ground, mud, soil and rain. This platform needs to be adaptable to other tasks and other crops,

• Intelligent inspection to decide which targets are appropriate for robotic manipulation. For example, robotic picking is vastly more efficient if only produce of the correct size and colour is picked. Similarly, robotic pollination is most efficient when only female flowers with a suitable spacing are selected for pollination,

• Produce handling; many horticulture products need to be handled very gently once they have been harvested as a drop over even a small distance may cause bruising.

• Obstacle avoidance: computer vision recognition of obstacles such as people, poles, wires, stumps and rocks so that an autonomous robot can navigate safely around these. • Swarm behavior management: to allow multiple autonomous robots to function together in

one area without interfering with each other,

• Cost; most of the horticultural tasks, such as fruit picking, only last for a few months of the year and it is not cost effective to use a robot for such a short period. Ideally, robots should be capable of performing many different operations, such as picking followed by bud count followed by pollination followed by fruit count, in order to extend the useful work period of the machine and ensure a reasonable payback period, and

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• Maintenance; As the majority of the horticulture industry is located in rural Australia often in remote areas, it is important to be able to provide the MARRS maintenance and

servicing infrastructure. This introduction of MARRS technologies will require a significant up skilling and training program in order to maintain these new and emerging technologies and systems. But it also provides an opportunity to retain and attract a younger workforce to rural Australia that have an interest in computing and the associated skills involved with automation.

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HORTICULTURAL FIELD/ORCHARD APPLICATIONS

Precision farming is defined as the application of technologies and principles to manage spatial and temporal variability associated with all aspects of agricultural production. It is a

comprehensive approach to farm management designed to optimise agricultural production through the use of information technology that brings data from multiple sources to bear on decisions associated with agriculture production. Precision farming has also been termed as Precision Agriculture (PA), Variable Rate Technology (VRT), spatially variable farming (SVF), GPS based agriculture, Site Specific Farming (SSF), Site Specific Management (SSM).

In Precision Agriculture, the field is broken into “management zones” also called ‘grids’ based on soil pH, nutritional status, pest infestation, yield rates, and other factors that affect production. Management decisions are based on the requirements of each zone and tools such as

Geographic Information Systems (GIS), Global Positioning System (GPS), are used to control zone inputs.

Geographic Information Systems (GIS) and Global Positioning Systems (GPS)

Geographic information systems (GIS) are a means of integrating data acquired at different scales and time and in different formats and can be considered to be a collection of spatially referenced data that act as a ‘model’ of the field.

A Global Positioning System (GPS) is a burgeoning technology, which provides unequalled accuracy and flexibility of positioning for navigation, surveying and GIS data capture. Its

development makes high accuracy spatial data easier to obtain in less time. It has a tremendous amount of application in GIS data collection, surveying and mapping. It uses satellite and

computers to compute positions anywhere on earth. As a result, numerous observations and measurements can be taken at specific positions and GIS can be used to create field maps based on GPS data to record and assess the impact of farm management decisions.

A GPS receiver requires at least four satellites to determine its position. However the raw GPS signal is not sufficiently accurate to determine position within a field. An additional signal from a known position (reference) is needed to provide the necessary accuracy, which can come from a land-based reference signal.

Remote Sensors

Sensors are devices that transmit an impulse in response to a physical stimulus such as heat, light, magnetism, motion, pressure and sound. By definition, remote sensing takes place without physical contact with an object, so any of the visual imagery, ultrasound, and reflectance etc processes fall under this category. The most common remote sensing options are aerial and satellite imagery, but technically speaking, it would include things like tractor mounted N-sensor, Weed seeker and close range laser etc. Proximal sensing makes use of contact sensors such as buried soil moisture sensors, load cells etc.

However, as is usually the case in agriculture, nobody takes a lot of notice of the strict technical definition, and remote sensing is taken to encompass aerial and satellite imagery used for farm planning and vegetation monitoring/management, and proximal sensing is taken to cover all those things that are "close" to the target, such as tractor mounted sensors (even if they are not in contact with the target) and, of course, those that actually make contact.

They can be contact or remote ground based or space based and direct or indirect. They have been developed to measure and monitor soil properties, crop stress, growth conditions, yields, pest and disease infestation, atmospheric properties, water, machinery, etc., used in precision agriculture. They provide the grower with instant (real time) information that can be used to adjust or control operations. They can be used to measure soil and crop properties as the tractor passes over the field, or as an airplane or satellite photographs the field from the sky.

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Remote sensors are generally categorised as aerial or satellite sensors that can provide instant maps of field characteristics. An aerial photograph is optical sensors that can show variations in field colour that correspond to changes in soil type, crop development, field boundaries, roads, water, etc. In this case, it can be either Passive RS where sensors detect the reflected or emitted electro-magnetic radiation or Active RS where the sensors detect the reflected responses from objects that are irradiated from artificially-generated energy sources such as radar.

One such company providing Remote Sensor technologies both for fruit quality assessment as well as airborne large scale Near Infrared (NIR) assessment of agriculture production is

Integrated Spectronics Pty Ltd (http://www.intspec.com/). Integrated Spectronics is an Australian based company established in 1989 to develop and manufacture electro-optical instrumentation for the mining, remote sensing and environmental industries. The company has now expanding its product range into agricultural applications such as product quality assurance and on-line process monitoring and control. The company has developed high performance, hyper spectral imaging spectrometers for earth resources remote sensing and field portable NIR spectrometers. Integrated Spectronics also provides electro-optical consulting services and contract R&D

services. The company’s products include Field Portable Spectrometers, new novel handheld Vis/NIR spectrometers for quality assessment of fruit. The company also supplies Airborne Hyper-spectral Imaging.

Another Brisbane based company providing airborne remote sensing services to the agriculture and horticulture industry is V-TOL Aerospace Pty Ltd7 (www.v-tol.com). V-TOL has a range of commercial and military grade products and services that provide basic point-to-point to sophisticated networked surveillance, and remote sensing capabilities for the horticulture industry. The company has a focus on collecting and networking spatial information using Unmanned Airborne Systems (UAS) and Unmanned Ground Sensors (UGS).

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Figure 2. Typical flight paths of Unmanned Aircraft.

Electromagnetic sensors

Scientists at the National Physical Laboratory (NPL) in the United Kingdom are working with KMS Projects and Vegetable Harvesting Systems (VHS) Ltd8 to develop technology as a foundation for intelligent harvesting machines, which can look beneath the leafy layers of a crop, identify the differing materials, and enable precise size identification. This can be used to develop a fully automated harvesting robot.9

The most appropriate technologies to use are radio frequencies, microwaves, terahertz and the far-infra red. These four parts of the electromagnetic spectrum all have potential to safely

penetrate the crop layers and identify the size of the harvestable material for a relatively low cost. NPL are developing a methodology for crop identification and selection focusing specifically on cauliflower crops, one of the hardest crops to measure due to the large amount of leafage that covers the vegetable. Researchers at NPL began by modifying microwave measurement systems to measure a cauliflowers structure. A series of measurements made on real crops in the

laboratory and field enabled a statistical range of measurements for precise size identification. This data is then designed into an algorithm to enable a simple size indication from a raw measurement with uncertainties. The final technology will be developed for a first generation harvester and tested in a real farming environment.

A successful demonstration of the imaging technology was given recently at the Fanuc Robotics site in Coventry, United Kingdom, showing its huge potential for the harvesting of cauliflowers, lettuces and other similar crops. Commercial support has been received to take the project forward and develop the complete product from one of the largest lettuces grower in the United Kingdom.

Automated/Mechanical harvesting

Automated harvesting is governed by the environment in which a particular horticulture crop is grown. Different farming systems, different horticulture production systems and different products will usually require vastly differing harvesting systems.

Scarfe et al. (2009) briefly reviewed the literature for semi-robotic systems to not only harvest crops but also in the on-going preparation and maintenance of the crop prior to harvest. Briefly, they describe a manure spreader with GPS guidance that is capable of spreading manure on a field under remote control by an operator 1 km away from the field10. In automatic weed control systems, the reviewers note that while three of the contributing core technologies (guidance, precision in-row weed control and mapping) are used commercially in non-robotic agricultural applications, the key area still requiring development is in weed detection and identification11.

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The development of an effective and efficient harvesting system relies on a mechanical harvester matched appropriately to a modified growing system. This modified growing system can be in the form of plant varieties “tuned” to the harvester or plant structure trained to match mechanical harvesting processes. The Matilda Fresh Foods case study demonstrates this requirement (see below).

Mechanical harvesting provides a number of advantages over traditional hand-picking, primarily: (1) decreased risks of contamination by human contact at the field; (2) decreased labour needs; (3) flexibility on speed and timing of harvesting; and (4) the ability to work at night. For the Australian horticulture industry, the major points of interest are the ability to increase harvest rates and the reduction of workers in the field, with the associated cost reduction in salaries, training, sanitary measures on field and lifting aids, among others. These characteristics make mechanical harvesting a very appealing proposition for farms which traditionally experience labour shortages during the harvest season. 12

A study undertaken by Dr Allan Twomey, HAL project VG05073, "Mechanical harvesting of selected vegetables -feasibility study"13 investigated the feasibility of mechanical harvesting of selected vegetables. Mechanical harvest systems have been developed for carrots, potatoes, radishes, beetroot, iceberg lettuce, celery, cabbage, brussels sprouts and cauliflower. This report found that the main interest in Australia is for mechanical harvesting which is targeted at in-field harvesting, grading and further processing all at the one site. The report also indicated that there was less interest in the development of sensors to evaluate the readiness of crops for harvesting. In summary, the main issues with automated harvesting in orchards or in fields is (1) the

precision capability of the visioning system for recognition of the ripe produce for harvest, (2) the selection of a grasping device (‘end effector’) which will not damage the produce, and (3) the navigation of the harvester or autonomous robot through the orchard or field (which rely on GPS technology or computer vision)14. Some of these aspects are considered in the case studies for this section.

Project VG 05073 provided some benchmark cost benefit estimates for mechanical harvesting. The report indicates that for each crop an expenditure of about $3 to $5 million over a 3 to 5 year period should be expected. This cost includes agronomics, harvester development, materials handling systems, mechanized /automated minimal processing, logistics and marketing. The cost also includes system development in at least two regions (thus including variation of geography-dependent factors). The report also provided some estimated gains achieved through mechanical harvest, based on marketable production with the specific quality required by buyers. These estimates are presented in the Table below. The results show a particularly attractive proposition for cauliflower, leafy greens and tomatoes.

Crop Benchmark yield range

(kg/ha) % harvesting cost reduction targets Estimated improvement

Asian brassicas Depends on type 70-78 $5,000,000

Broccoli (fresh market) 15,000 to 20,000 65-75 $6,000,000 Broccoli (processing market) 19,500 to 24,000 68-75 $4,000,000 Celery 48,000 to 52,000 55-65 $5,000,000 Cauliflower 40,000 to 45,000 50-70 $30,000,000 Leafy greens 20,000 to 27,500 55-65 $24,000,000

Tomato Roma and bunches

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A recent study in the US15 compared selective and non-selective mechanical harvesters for asparagus in terms of efficiency levels, profitability, and yield harvested. A bio-economic model was used to determine the impact on profits and harvested yields by the two mechanical harvesters and their levels of profitability compared to manual harvesting. The results showed that at the efficiency levels observed for the selective mechanical harvester (i.e. 80% of spear recovery rate, 5% of damage rate to the existing spears, and 5% of damage rate to the harvested spears) the profit generated by mechanical harvesting was $1,497/ha, lower than the profit observed for manual harvesting ($1,666/ha). However, the results suggested that further development of the selective mechanical harvester would likely lead to higher profitability ratios than those found for manual harvesting.

The development of automated and mechanised harvesting systems for the horticulture industry has been underway since as the late ‘60s. Dr Estrada-Flores from Food Chain Intelligence has compiled of list of over 225 patents globally on harvesting system inventions for the horticulture industry from 1969 to 2009.16 The critical issues to consider here is: of these patents how many have been successfully commercialised?

Of these 225 cited mechanical harvesting patents 40% were registered US Patents, 28% from Japan and only 2% were Australian registered patents.

Case Study - Automated Broad Acreage Harvesting of Broccoli: Matilda Fresh Foods

Matilda Fresh Foods was Australia’s largest private exporter of broccoli and onions. Located on the rich pollution free volcanic soils of the Darling Downs, Matilda created a strong Asian presence through high quality products and excellence in delivery to customers. Matilda was also the major producer of fresh, ready to cook and eat, chilled broccoli and cauliflower florets under the “You’ll Love Coles” brand to Coles supermarkets throughout Australia. Matilda utilised a systems approach to the selection, growing, harvesting, processing, transport and logistics and marketing of broccoli products. The innovative components of their production system were:

• Varietal selection using international benchmarks for productivity and harvestability. Growing systems developed for mechanical harvesting that would build on existing technology for GPS cultivation, seed treatment and irrigation. The innovation was in the development of a production system that integrated with the mechanical

harvesting system.

• The Mechanical harvester was the centre piece of their innovative system. The machinery could harvest heads and stems and mechanically separate and floret them in field. This effectively reduced harvest cost and the transport of the biomass.

This Case shows the essential element of the development of automated harvesting is to match the crop agronomy system with the automation. The agronomy system required the selection of the appropriate broccoli varieties that were tall enough for the harvester and growing practices that involved planting the seeds and seedlings in rows relative to the path of the sun to encourage further tall growing plants.

The Harvester capability exceeded the anticipated output which led to a complete revision of the handling and processing systems. The harvester could harvest 56,000 broccoli heads per hour and in addition allowed the automated harvest of material that would previously have remained in the field.

An appropriate Business Model was being developed to enable the commercialisation of the Broccoli harvester within the Matilda Farm business. The different models considered were;

– License an existing harvester manufacturing company to build, sell and service Broccoli Harvesters.

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– Build a limited number of harvesters and establish a contract harvesting company. – Separate license agreements for different markets.

Consideration of the potential business models that are required in order to successfully commercialise outcome of any MARRS development is crucial to successful adoption of these technologies in terms of economic returns.

Figure 3. Broad acreage Broccoli Harvester

Case Study – Robotic Harvesting in Orchards: Carnegie Mellon University’s Robotics Institute

Two groups of researchers at Carnegie Mellon University’s Robotics Institute received $10 million in grants from the U.S. Department of Agriculture (USDA) to build automated farming systems. One is for apple growers and one is for orange growers, but both are designed to improve fruit quality and lower production costs.17

The projects are funded through the USDA’s new Specialty Crop Research Initiative. The

Comprehensive Automation for Specialty Crops (CASC) Program is led by Sanjiv Singh, research professor of robotics. The Comprehensive Automation for Specialty Crops (CASC) program is a multi-institutional initiative led by Carnegie Mellon Robotics Institute to comprehensively address the needs of specialty agriculture focusing on apples and horticultural stock. CASC is a

syndicate of research and commercial companies that have been funded by the USDA

Cooperative State Research, Education & Extension Service with matching support from industry and the Pennsylvania Infrastructure Technology Alliance. The Syndicate consists of the Carnegie Mellon University, The Pennsylvania State University, Washington State University, Vision

Robotics Corp, Oregon State University, and Purdue University

CASC is developing methods to improve production efficiency, identify threats from pests and diseases, and, detect, monitor and respond to food safety hazards.18

Harvesting remains one of the most labor-intensive operations at orchards, but it also is very challenging to automate because of demanding handling requirements and the associated cost requirements. Both projects will investigate new designs for mechanical harvesters, including a vacuum-assisted device that the CASC will use for apple harvesting, but the emphasis will be on aiding human harvesters, rather than replacing them.

CASC will focus on apple and ornamental and tree fruit nursery production in Pennsylvania, Washington and Oregon. These states alone have 200,000 acres of apples (60% of the US fresh apple production) produced on about 6,000 farming operations with a farm gate value of US$1.4 billion. These crops are representative of specialty crops grown across the US.

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The key industry needs that are being addressed are: early detection of diseases and insects, monitoring of plant health, assessment of crop value, reduction in the amount (and cost) of sprays and nutrients, increase in the efficiency of labor, and reduction of damage to crops at harvest. CASC’s goal is to work with the specialty crop industry to fulfill its vision of significantly reducing the cost of production of US fruit, which is fundamental for its survival. Objectives include developing, integrating, testing, deploying, and assessing a carefully chosen set of information, mobility, manipulation and plant science technologies, assessing their socio-economic utility, and transferring results to the end users via commercialization and extension.

Figure 4. Autonomous Prime Mover for deployment of sensors, based on a Toro Workman MDE utility vehicle

CASC are also developing a crop load estimation system for medium- to high-density orchards.19 The system scans fruit trees to determine the total crop yield, the size, location, and color of each piece of fruit in the orchard. The existing prototype has been able to demonstrate a proof-of-concept. Design of the first full prototype is currently underway, which will include the form and function of the anticipated design but requiring more evolution to reach the production design. The goal is to create a system that accurately detects more than 95% of the fruit in a typical orchard. Much more research work is required before the system is ready for commercial application.

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Figure 5. Vision System detection of Red Apples.

The Integrated Automation for Sustainable Specialty Crop Farming Project, of the Robotics Institute’s National Robotics Engineering Center (NREC), received a three-year, US$4 million grant to develop systems for the citrus industry. Both project grants are being matched dollar for dollar by industry, state governments and other funding sources.

The NREC’s Integrated Automation for Sustainable Specialty Crop Farming Project will deploy a fleet of networked, unmanned tractors in the orange groves of Southern Gardens Citrus (SGC), one of Florida’s largest growers. In addition to SGC, collaborators include researchers at the University of Florida, Cornell University and John Deere & Co.

Penn State University in the United States is also undertaken related work on innovative technologies for the thinning of fruit trees. Tree fruit are thinned at the blossom or early fruit development stages to ensure larger, higher quality product. This management practice, typically performed by hand, is a labor-intensive and expensive activity. Development of methods to mechanize thinning is a top priority for the tree fruit industry. This project will develop and test new mechanical thinners.

Specific project objectives are:

• Integrate mechanization and tree canopy architecture (as growing systems evolve from a three-dimensional to two-dimensional structure) by investigating training modifications to make flowers or fruit more visible/accessible and new methods of targeting optimum level of crop load adjustment at various stages of bloom/fruit development,

• Further develop and modify two prototype non-selective fruit thinning devices to improve prototype efficacy and commercialisation potential,

• Develop and integrate electronic and mechanical technologies for higher precision and selective thinning,

• Provide technology transfer by pilot testing in orchards with commercial growers.20

The Case Study demonstrates the complexity of some of the MARRS solutions that will be required in the horticulture industry. It also demonstrates that development of robotic picking of apples will require again developments in the agronomy and growing systems. This could be apples grown in a trellis arrangement to provide better access for vision recognition and robotic manipulation for picking.

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This case also demonstrates the amounts of funding that the United States industry and government are willing to apply to the development of MARRS solutions for horticulture. It also shows the gap between research and successful commercialisation.

Case Study - Autonomous Robotic Kiwifruit Picking: Massey University

New Zealand researchers at Massey University have made significant progress into the development of an autonomous kiwifruit picking robot. Similar ‘agrobotic’ systems include a prototype orange picking robot being developed in Italy and an apple-picker being developed in Belgium. Neither of these is yet commercially viable and both use human operators to position the picking robot.21 In existing kiwifruit Packhouses, approximately 30% of the fruit is rejected on the basis of size and quality. The fruit growers pay the pack house a packing fee which is based on the gross tonnage with a fine for rejects. The ability of the vision software to recognise fruit which is undersize, unripe, misshapen or marked makes the system economically attractive to the growers. The Massey University researchers have developed a robot with intelligent vision system that ensures that only ‘good’ fruit is picked. The robot receives instruction by radio link and operates autonomously as it navigates through the orchard, picking fruit, and unloading full bins of fruit, teaching empty bins and protecting the picked fruit from rain.

Briefly, the kiwifruit picker employs a panoply of cameras: two are mounted looking forward and enable the picker to make its way around the orchard; two are mounted looking toward the rear to locate and handle the fruit bins. There are also a number of cameras that look up at the canopy to coordination with the picking arms.

The robot platform being developed is an autonomous 4-wheel drive robotic vehicle which performs the following functions:

• Uses GPS and intelligent vision to navigate kiwifruit orchards; manoeuvring around obstacles such as posts and recognising braces at the end of each row.

• Identifies fruit, discriminating for size and gross defects. Picks the fruit and places it gently into the bin. Checks the fruit level at each point in the bin and adjusts fruit placement to fill the bin evenly. Currently, the robot is able to pick at one fruit per second.

• Decides when the bin is full, goes to the end of the row and unloads the bin. Searches for and picks up an empty bin with its forks, returns to the last position and resumes picking.

• Operates 24-7, checks for light level and operates floodlights if necessary. Checks for rain or dew and covers the bin with a tarpaulin when this is detected so that picked fruit is protected.

• Goes into secure mode (for example when the fruit is wet), moving the robotic arms to a safe position, switching the unnecessary power systems off, and maintaining power only to the main (monitoring) computer and radio link. Wakes up when appropriate and resumes picking.

• Receives and responds to communications via radio link and uses voice recognition to respond to verbal commands.

• Uses a variety of recovery strategies to deal with faults such as getting stuck, vision becoming obscured, etc.

• Collects data on the fruit yield from a particular orchard.

The kiwifruit picking robot has spent the six months in an orchard, learning to navigate and to recognise and pick fruit. Fruit picking and handling are still in the experimental phase and

development will continue during the 2010 harvest. A video showing the unit operating one of the four robot arms is available at http://www.massey.ac.nz/~rcflemme/current%20projects.html

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Figure 6. Autonomous Kiwifruit Picking Robot.

To increase the utilization of the robot, researchers now intend to focus on the robots to also pollinate flowers. Pollination is an expensive and difficult operation in kiwifruit orchards and unexplained bee-hive deaths are a considerable worry to orchardists, therefore some orchardists apply pollen manually so that they are not reliant on bees. Manual applications do not apply the pollen efficiently. The vision system on the automated kiwifruit robot will be developed to

recognise female flowers and apply pollen precisely to the flower in an optimal manner (leaving sufficient room between pollinated flowers for the fruit to develop in an unobstructed way) using a customised pollen delivery system attached to the robot hand.

The pruning of kiwifruit vines is another expensive and time-consuming operation for the industry. The autonomous robotic system will be adapted to perform this function. The robotic arms of the system will be adapted to pick other types of fruit such as apples and oranges.22

This particular case study demonstrates that developing MARRS solutions that have more than one function makes the introduction of the technology economically viable. In this case the same autonomous robot platform for Kiwifruit will be used for fruit picking, pollinating flowers, spraying and other crop maintenance activities. The platform will also have spillover effects into the wider horticulture industry as it is adapted to harvest other crops such as apples and oranges.

Case Study – Autonomous Robotic Strawberry Picking: Magnificent Pty Ltd

Faced with the ever increasing issues in the labour market, Glass House Mountains strawberry farmer Ray Daniels of SunRay Strawberries Pty Ltd, on the Sunshine Coast north of Brisbane, discovered a solution to the challenges faced by strawberry growers while listening to a

presentation on robotic harvesting by agricultural engineer Rudi Bartels at a strawberry growers meeting in 2007.23

Harvest labour costs are the single largest cost item for strawberry growers, accounting for more than half of the total cost of production. The large seasonal labour force that is required also brings many management issues that dwarf the challenges of strawberry cultivation. Issues include the time and costs involved in labour recruitment, language barriers, illegal migrant issues, management of rapidly fluctuating labour requirements, unreliability of low-paid labour, provision of accommodation, transport, medical care, payroll administration, social order costs,

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The Universities of Okayama, the University of Utsunomiya and the Universidad Politécnica de Madrid are all developing harvesters for hydroponically grown strawberries. These competitors do not currently pose a direct threat to Magnificent Pty Ltd as they target a niche market that

accounts for less than one per cent of world strawberry production.

The University of Miyazaki in Japan and Robotic Harvesting LLC (Limited Liability Company) in California are also developing robotic strawberry Harvesters for field grown strawberries. Prof Nagata leads the University of Miyazaki research however has to date been limited to laboratory testing of a robotic gantry. As this research is academically focused and with Prof Nagata semi retired, the Miyazaki Harvester is not perceived as a competitive threat in the medium term24. This Case Study demonstrates that there is a large gap between MARRS technologies developed in the research environment and what is required to operate in a commercial environment. It also highlights the importance of researchers being able to understand the commercial operation associated with growing and harvesting produce.

Case study - Automatic Weed Control System for Transplanted Processing Tomatoes Using X-ray Stem Sensing: USDA Agricultural Research Service

Recently, a stem detection system was developed for automatic weed control in transplanted tomato fields. This research was conducted through the USDA Agricultural Research Service for the project “Sorting agricultural materials for defects using imaging and physical methods”.25 Briefly, a portable x-ray source projected an x-ray beam perpendicular to the crop row and parallel to the soil surface. The plant’s main stem absorbs x-ray energy, decreasing the detected signal and allowing stem detection even in the presence of leaves. This signal is used to control the operation of a pair of weed knives. Minimizing the source to detector distance as the system moved along the row allowed for differences in signal strength between stems and background as high as 180 mV (vs. background noise levels around 30 mV) at low x-ray energy and current levels (25 keV, 7 mA), which is a significant advantage for safety reasons. The detector consisted of a linear array of photodiodes aligned perpendicular to the soil. This configuration helps

differentiate branches, which are angled and block only some of the photodiodes, and stems which have the same vertical alignment as the array and hence block all photodiodes. A field trial was conducted in a 15 meter section of row containing 39 tomato seedlings. At a speed of 1.6 km/h, the detection system identified all 39 stems of standing plants with no false positives.26

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PROTECTED CROPPING (GLASSHOUSE/GREENHOUSE)

APPLICATIONS

Horticulture Australia estimates that Australia has 1,600 ha of protected cropping systems for vegetables. AUSVEG data indicates that in 2006-07 there were 870 ha of protected crops. All indications are that the protected cropping industry is growing fast, at a rate of at least 6% per annum. It is expected that the planted area will treble by 2017, particularly in SA and NSW. Protected vegetable production is currently focused on cucumbers, capsicums, hydroponic lettuces, herbs and tomatoes.

The structured environment of protected cropping with its high plant density and high product value, often justify the expense of robotic picking applications. In their brief review of the literature, Scarfe et al. (2008) describe research for an autonomous robot for harvesting cucumbers. However, only 80% of the cucumbers are picked and the average pick-rate is 45 seconds per cucumber27. Belforte et al. (2006)28 undertook a review of robotic harvesting of mushrooms, lettuce and strawberries in greenhouses and note that these are not commercially viable because they are too specific in their purpose (picking is typically a very short period in the life of the crop) and have an unattractively slow pick-rate. They developed a proof-of-concept stationary robot capable of under-leaf spraying and precision fertilization of potted plants which were moved on a conveyor past the stationary robot with a cycle time of 7 to 8 pots per minute. Although the dual functions of the robot mean that it can be used for a greater portion of the plant life, the cycle time is too slow for commercialisation and the problems associated with moving the plants rather than the robots are large29.

E.J. van Henten30 in an analysis of a generic crop production process combined with a review of the state of the art in greenhouse mechanisation revealed that the first phases of plant production such as seeding, cutting, grafting and transplanting as well as the final phase of crop production including sorting and packing the harvested produce are already mechanised. Those tasks do not require much human intelligence and/or fast and accurate eye-hand coordination. The available machines are largely based on principles of industrial automation consisting of mechanical solutions with only a limited amount of sensors and ‘intelligence’ used. The next ten years, the available line of machines will be redesigned, extended and optimised.

The middle phase of crop production including crop maintenance and harvest has little or no automation as yet. Maintaining the crop and harvesting rely on human intelligence and ability and are much more difficult to automate. The next ten years will show the advent of the next

generation machines that combine smart mechanical design with sensors and ‘artificial intelligence’ to achieve the fast and accurate eye-hand coordination needed for these difficult tasks. This trend is supported by the commercial development of a strawberry harvester in Japan and a rose harvesting robot and tomato de-leafing robot in the Netherlands. van Henten

concluded that progress is slow in the field of robotic harvesting due largely to uncertainty in the working environment of the robot as a result of biological variability and the typical structure of the growing systems used. Progress in the field of greenhouse robotics therefore will not only rely on innovations in the field of robot technology but also on necessary innovations in the field of growing systems and plant breeding to reduce variability and thus to simplify the task.

Case Study – Protected Horticulture Robot Harvesting: Bio-oriented Technology Research Advancement Institution

In Japan, protected horticulture has traditionally relied on small-scale, labor-intensive cultivation practices managed mainly by family members. However, large-scale greenhouses are gaining popularity, and serious enterprise-like management with an emphasis on productivity and employment is spreading. The trend toward larger greenhouses could be accelerated by the development and introduction of MARRS solutions.

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The Horticultural Engineering Department at the Bio-oriented Technology Research

Advancement Institution (BRAIN) and the National Institute of Vegetable and Tea Science of the National Agriculture & Food Research Organisation in Japan have developed 3D vision and harvesting systems for strawberry, eggplant, and tomato in the Greenhouse environment. The Research teams have developed three types of prototype robots for mechanisation of protected horticultural production. The vision detection algorithms and the harvesting

end-effectors were designed to suit each of the target fruits. Color information was used for strawberry and tomato, and size was used for eggplant. The final design of harvesting robots for practical commercial use needs information on crop features (color, shape, and size) and the development of appropriate crop maintenance and environmental conditions.

Figure 9. Hydroponic Strawberry Harvesting robot

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Figure 11. Eggplant-harvesting robot

Figure 12. Robot Arm picking Egg Plant.

Case Study - Robot Picking of Sweet Peppers in a Greenhouse: Kochi University of Technology, Japan

A picking robot for sweet peppers in greenhouse horticulture has been developed by the

Intelligent Mechanical System Engineering Department in Kochi University of Technology, Japan. The developed prototype robot has the following; a recognition system and a cutting system. In the recognition system, researchers used image processing with lighting and stereovision. 31 To achieve their objectives, the picking robot has an image processing system with a parallel stereovision, a positioning system to follow the recognized sweet pepper by visual feedback control, and a cutting device. Experiments were initially conducted in the laboratory. First

experiments were carried out without leaves, and recognition of the fruit and cutting the stem was successful. The second experiment was carried out with leaves. In this situation, recognition was successful, but the success rate of cutting the stem became low because leaves covered the stem.

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Figure 13. Structure of Laboratory Prototype Sweet Pepper picking robot.

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Figure 15. Sweet Pepper visual feedback control image.

Both of the above Case Studies demonstrate that development of MARRS solutions maybe achieved quicker by operating in the more structured environment of a Greenhouse. Variation in plant structure and growth is more controlled in this environment and the technology can be protected and have control functions reduced in complexity if operated on fixed tracks.