Prune news APIA awarded funds for tech update

APIA awarded funds for tech uptake

The Australian prune industry has been successful with a grant application to promote adoption of technology and increase grower profitability via a new grant application to AgriFutures Australia.

Early in 2021, AgriFutures Australia launched a pilot Producer Technology Uptake Program to support eligible producer groups to increase adoption of technology solutions on-farm. The pilot was a success and the rural research facilitator has recently invited applications for round three of the program.

Australian Prune Industry Association deputy chair Michael Zalunardo said APIA had successfully applied for a round-three grant to look at fruit scanning technology and its suitability in prune crops.

“The problem is that the Australian prune industry produces a large amount of small fruit that doesn’t meet the premium price market channel,” Michael said.

“In recent years we have seen the market move towards pitted products. Unfortunately, small fruit is not suitable for pitting and it is increasingly difficult to find a buyer for this fruit, so the prices are low and often not sustainable for growers.

“The solution is to balance crop load to boost both the number of fruit and the size of fruit harvested so a greater proportion meets premium price grades and maximum profitability is realised.

"However, this can be tricky. If you thin too much fruit from trees, individual fruit size will increase, but you run the risk of having too few fruit and thus decreasing overall yield. Conversely, if you leave too much fruit on the tree, each fruit receives a smaller share of the finite nutrient supply resulting in small fruit.”

APIA was unsuccessful with its grant application to the 2021 AgriFutures Australia pilot program but succeeded with a revised application under round three of the Producer Technology Uptake Program.

The revised project aims to help address the problem of crop load and thinning by demonstrating fruit scanning technology as a means of accurately determining the crop load and mechanical thinning requirements.

Michael said there were many reasons why growers had been slow to adopt on-farm technology to address the fruit size problem.

- A reluctance to thin as current measurements are time consuming and use a small sample size

- Unfamiliarity with available technologies

- Cost to implement new technology (usually an up-front cost)

- Scepticism about thinning (and the lack of hard data in prunes)

- Reliance on contract tree-shaker operators for thinning (which is expensive)

- Opting to “roll the dice” and decide against thinning in the hope natural thinning will be sufficient.

The APIA project hopes to overcome some of these barriers by trialling Green Atlas’s Cartographer platform in prune orchards.

Green Atlas co-founder Dr Steve Scheding said the mobile platform had been independently validated by Agriculture Victoria in commercial apple, pear and stonefruit orchards but was relatively new for prune orchards.

“Cartographer is a mobile hardware and software platform capable of simultaneously counting the number of visible fruit and nuts in the trees (using computer vision) whilst also building three-dimensional models of those trees (using LiDAR),” Steve said.

“Both attributes are known to be highly correlated to yield.”

The Cartographer scanning unit is mounted to a fast All-Terrain-Vehicle (ATV) and driven along orchard rows at high-speed to gather data at treelevel.

Steve said the initial process was relatively fast, with a platform capable of scanning six to eight hectares per hour in orchards with four metre row spacings.

This data is then uploaded to the Cartographer processing unit, which uses state-of-the art algorithms to process the data into precise spatial density maps of fruit number and predicted yield maps.

Michael said the trial would use scanning technology to identify crop load for thinning, but the resulting map is also a useful tool in overall orchard management.

“The spatial pictures allow growers to identify areas that are not performing well and require targeted strategies to help achieve uniform yield and fruit quality characteristics,” he said.

The Producer Technology Uptake Program is intended to provide funding for groups of producers to develop a technology adoption program to support greater technology uptake on-farm. Successful grant recipients will work with individual producers within their group to deliver activities that provide them with information and knowledge needed to overcome technology adoption barriers.

The project activities will be undertaken around Griffith, NSW where the Australian prune industry is now largely located and would involve both larger (>150 tonnes) and smaller (<50 tonnes) growers. AgriFutures Australia will work closely with successful groups to support the rollout of program activities. A key part of the program is to leverage learnings across groups and ensure there is peer-to-peer learning across producers.

Michael said that Cartographer would be used to scan up to five different orchards, with ground-truth counts obtained through selective harvest. Following data analysis and report preparation, the plums would be thinned accordingly.

At the end of the season, the technology will be validated with quality assessment, with case studies produced to inform growers of all sizes about the potential benefits arising from the use of the Green Atlas technology. v

Phil Chidgzey APIA national secretariat secretary@ausprunes.org.au M: 0439 733 321

Left page: The Cartographer platform scanning a prune orchard.

AUSTRALIAN TREE CROP october/november 2021

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tree rows (i.e., calibration zones). Once the calibration factor is applied to the originally detected fruit number per image, accurate maps of fruit number per tree can be generated. Pioneering research for field estimations of fruit number, fruit diameter and fruit colour in stone fruit was conducted by Agriculture Victoria. The reliability of Green Atlas Cartographer was first tested at the Tatura SmartFarm and then evaluated in three commercial orchards (Varapodio S T & R, Ardmona, Victoria). Work at the Tatura SmartFarm was performed in 2020–2021 on apricot (‘Golden May’), plum (‘October Sun’), peach (‘Snow Flame 23’, ‘Snow Flame 25’ and ‘August Flame’) and nectarine (‘Majestic Pearl’ and ‘Autumn Bright’). Evaluation on commercial orchards was carried out on ‘Glacier’ peach, ‘Summer Flare 34’ nectarine and ‘October Sun’ plum. The Green Atlas stone fruit detection algorithm was trained on the images collected at the Tatura SmartFarm. Results obtained at the Tatura SmartFarm showed that overall, under different training systems, the error of estimates of fruit number per tree was ≤10% in all the crops under study (Table 1).

Crop Training System Estimation error (%)

Apricot Tatura Trellis 2 Vase 3 Plum Tatura Trellis 2 Vase 5 Peach Vertical leader 4 Tatura Trellis 4 Nectarine Cantilever trellis 1 10 Cantilever trellis 2 8 Tatura Trellis 5 Vertical leader 6 Table 1. Estimation errors of fruit number per tree predicted with Green Atlas Cartographer in apricot, plum, peach and nectarine trees under different training systems at the Tatura SmartFarm. The evaluation of prediction models in the three commercial orchards also highlighted good predictions of fruit number per tree; the estimation errors in this case were 8, 7 and 6% for nectarine, peach and plum, respectively. An example of a spatial map of fruit number per tree produced in the commercial nectarine orchard is shown in Figure 2. Fruit diameter estimations did not require the additional calibration step needed for fruit number predictions. The accuracy of the prediction was very good (>90%) and the estimation error was <5mm, regardless of crop, row spacing, training system and fruit vertical position in the canopy (i.e., low, medium and high). Figure 3 shows a pooled dataset of fruit diameter predictions in different crops and cultivars.

FEATURED CROP – POME & STONE FRUIT Estimating stone fruit number, size and colour Alessio Scalisi, Muhammad S. Islam, Mark O’Connell* The application of fruit recognition techniques based on artificial intelligence provides the opportunity to estimate several crop parameters in orchards. Detected fruit can be used to obtain fruit number per tree, fruit size and skin colour. There is a growing demand for in situ estimations of fruit number, fruit size and fruit colour to improve thinning, harvest logistics and fruit quality, and potentially reduce fruit grading and handling costs. In addition, smart mobile platforms that can rapidly estimate high-resolution crop parameters need to be able to generate relatively accurate predictions in orchards with diverse tree training and spacing designs and in multiple cultivars. Recently, Green Atlas developed Cartographer, a mobile platform that has been successfully used to rapidly scan commercial orchards and produce flower and fruit counts (up to 6–8 ha per hour in orchards with approx. 4m row spacings). Green Atlas Cartographer (Figure 1) is an innovative combination of hardware (e.g., GPS, LiDAR, high-resolution RGB-cameras) and software, based on machine learning algorithms. This new technology offers the summerfruit industry opportunities to measure fruit number, size and skin colour. After scanning the orchards with Cartographer, precise spatial density maps of fruit number can be produced. To obtain accurate maps with absolute values of fruit number per tree, a further calibration step is needed. Calibration is commonly carried out on a small number of short (7–10m) sections of Figure 1. Green Atlas Cartographer in a nectarine orchard at Varapodio S T & R. R&D/CROP LOAD MANAGEMENT At an ANP-0131 (marketed as Ricó) blush pear field walk at his Calimna Orchard in November, Ardmona senior fruit grower and industry leader Matthew Lenne made clear the practical value of a greater understanding of spatial variability: “Understanding spatial patterns of flowering and fruiting will enable growers to prioritise and plan thinning management,” he said. Technology that helps quantify spatial variability in flower and fruit numbers and maps it for ease of viewing, can deliver that understanding and be used to consistently optimise crop load season after season. Spatial estimates of flower and fruit numbers are fundamental to optimising management strategies such as thinning and data has the potential to be interfaced with variable rate chemical thinning sprayers for high-precision, automated management. Predicting flower and fruit number The application of flower and fruit recognition techniques based on artificial intelligence provides the opportunity to estimate several crop parameters in orchards. However, only a few companies have commercial ground-based or aerial platforms capable of accurately predicting crop parameters. The Australian based company Green Atlas targeted this market gap and recently developed Cartographer, a ground-based mobile platform that has been successfully used to rapidly scan commercial orchards and produce flower and fruit counts in apples. Scanning with Cartographer can very quickly produce a ‘relative heatmap’ of flower and fruit number per tree that clearly shows the variability across an orchard block. For more accurate determination of the absolute numbers of flowers or fruit, Cartographer requires calibration for a specific block. Calibration requires ground-truth counts of flower clusters and fruit in calibration zones. In most cases, spatial and temporal variability within orchard blocks can be determined by simply assessing uncalibrated spatial maps. Flower and fruit variability in ‘ANP-0131’ pears The blush pear cultivar ‘ANP-0131’ produces fruit with superior texture, flavour and appearance, but is known to show a biennial bearing pattern. “Utilising tools like Cartographer can help growers mitigate biennial bearing and assess the success of crop regulation practices,” Matthew Lenne explained at the field walk. The Tatura SmartFarm hosts two experimental trials with ‘ANP-0131’ pears. The Planting systems experiment encompasses a combination of different tree spacings, tree architectures and rootstocks and is an ideal setup to visualise the spatial variability of flower and fruit number predicted by Cartographer. Crop load and yield variability in pear orchards represents one of the biggest challenges for growers. A significant part of the challenge is the difficulty of manually assessing variability across blocks and time and measuring the effectiveness of management approaches. Pioneering research at Agriculture Victoria’s Tatura SmartFarm and on commercial orchards is demonstrating the potential that new flower and fruit mapping technology has to improve understanding and better management of variability. The current work being undertaken is part of the PIPS3 Program’s Developing smarter and sustainable pear orchards to maximise fruit quality, yield and labour efficiency (AP19005) project being led by Agriculture Victoria. Crop yield variability Variable yield is seen both between and within blocks (spatial) and between seasons (temporal). Many factors influence crop load variability including inherent biennial bearing, weather and pollination. In orchards, both spatial and temporal variability need to be minimised to simplify management and achieve regular cropping. Cartographer maps path to uniform, high quality pears ALESSIO SCALISI, LEXIE McCLYMONT AND IAN GOODWIN New crop mapping technology has insights for managing crop load variability and improving packouts. Green Atlas Cartographer in the ‘ANP-0131’ planting systems experiment at the Tatura SmartFarm. About the authors: Alessio Scalisi, Lexie McClymont and Ian Goodwin. Tatura SmartFarm, Agriculture Victoria, Tatura, Victoria, Australia E: alessio.scalisi@ agriculture.vic.gov.au

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