Fruitometry is changing the kiwifruit industry by using unique AI for fast and precise orchard scanning. By implementing this technology, orchard owners and managers have visibility of their crops and gather information to improve yields, reduce labour costs, and make better decisions around crop management.
水果計量學通過使用獨特的人工智能進行快速精確的果園掃描，正在改變獼猴桃行業(yè)。通過實施這項技術(shù)，果園所有者和管理者可以了解他們的作物，收集信息以提高產(chǎn)量，降低勞動力成本，并就作物管理做出更好的決策。
And with approximately 2800 kiwifruit growers and 3200 registered orchards in New Zealand, there’s huge potential to build efficiencies and productivity in the kiwifruit industry.
新西蘭約有2800名獼猴桃種植者和3200個注冊果園，獼猴桃產(chǎn)業(yè)在提高效率和生產(chǎn)力方面具有巨大潛力。
The AI alternative to a manual process人工智能替代人工流程
In 2018, Chris Miller, founder and CTO of Fruitometry, used his vision technology and AI expertise to develop the Fruitometry Digital Crop Estimation (DCE) technology, a system to count kiwifruit as an alternative to the labour-intensive manual process. Joining him was Mike Ullrich, director and co-founder, who has over 25 years of experience leading high-growth global tech companies. The company has investment from Seeka and was the recipient of a Callaghan Innovation project grant.
2018年，F(xiàn)ruitometric的創(chuàng)始人兼首席技術(shù)官Chris Miller利用他的視覺技術(shù)和人工智能專業(yè)知識開發(fā)了Fruitometry數(shù)字作物估算（DCE）技術(shù)，這是一種計算獼猴桃的系統(tǒng)，可以替代勞動密集型的手工過程。與他同行的是董事兼聯(lián)合創(chuàng)始人Mike Ullrich，他在領導高增長的全球科技公司方面擁有超過25年的經(jīng)驗。該公司獲得了Seeka的投資，并獲得了卡拉漢創(chuàng)新項目的資助。
“Fruit growers have relied on manual counts forever,” says Chris Miller. “There was no alternative until Fruitometry offered our revolutionary service to the domestic kiwifruit market in 2019.”
克里斯·米勒說：“水果種植者一直依賴人工計數(shù)。”?！霸?019年Fruitometric為國內(nèi)獼猴桃市場提供革命性服務之前，別無選擇?！?/p>
He says that the primary barrier to adopting technology is a conservative mindset, with growers and managers tending to wait for others to find success with new tech before they will adopt it themselves. As Fruitometry offers radically new technology, it can present a chicken-and-egg dilemma, says Chris, so there is a focus on the value the technology brings to potential customers.
他說，采用技術(shù)的主要障礙是保守的心態(tài)，種植者和管理者傾向于等待其他人在新技術(shù)上取得成功，然后再自己采用。Chris說，由于水果計量學提供了全新的技術(shù)，它可能會出現(xiàn)雞和蛋的困境，因此人們關(guān)注的是該技術(shù)為潛在客戶帶來的價值。
“Our service fee is an investment that offers a substantial rate-of-return for growers to reduce their field costs, optimise their crop load, and improve their overall returns.”
“我們的服務費是一項投資，為種植者提供了可觀的回報率，以降低他們的田間成本，優(yōu)化他們的作物負荷，提高他們的整體回報?！?/p>
And while Fruitometry absolutely relies on AI, in the domestic market Chris says it’s the results that count, rather than a focus on the tech. “Orchard owners and managers care about their return from the dollars they spend, not the latest and greatest buzzwords.”
雖然水果計量絕對依賴于人工智能，但在國內(nèi)市場，克里斯表示，重要的是結(jié)果，而不是對技術(shù)的關(guān)注?！肮麍@主和管理者關(guān)心的是他們花的錢的回報，而不是最新、最流行的流行語?！?/p>
Better decisions, better outcomes for orchards and their owners
Fruitometry provides growers and managers with insights to make earlier decisions in their crop management. Within 24 hours of an orchard scan, the system generates a heatmap of the current fruit density per square metre for every row of an orchard. Scans can be done at all stages of the kiwifruit lifecycle. Scans measure winter buds to provide a check on the quality of winter pruning, shoots to assess bud burst rate for number management, flower buds to identify heavier loaded zones to inform a thinning strategy, fruitlets to help plan for thinning and to understand fruit variation across an orchard, and fruit to inform final thinning and estimate the harvest.
更好的決策，為果園及其所有者帶來更好的結(jié)果
水果計量學為種植者和管理者提供了在作物管理中做出早期決策的見解。在果園掃描后的24小時內(nèi)，該系統(tǒng)會生成果園每行每平方米當前水果密度的熱圖。掃描可以在獼猴桃生命周期的各個階段進行。掃描測量冬芽以檢查冬季修剪的質(zhì)量，測量嫩芽以評估芽爆裂率以進行數(shù)量管理，測量花蕾以確定負載較重的區(qū)域以告知疏伐策略，測量幼果以幫助計劃疏伐并了解果園中的果實變化，測量果實以告知最終疏伐并估計收成。
Field Units, mounted on quad bikes and ATVs, capture crop density information and orchard characteristics via a range of cameras and sensors. Data collected includes topographical data such as longitude, latitude, and altitude, and 3D image capture from multiple camera angles. For each scan thousands of photos are taken, then processed in real time using AI deep learning engine to identify buds, shoots, flowers, fruit, and canopy characteristics. To do this, Fruitometry uses an in-house processing cluster, rather than relying on slow and expensive cloud-based processing and storage services.
安裝在四輪摩托車和沙灘車上的田間單元通過一系列攝像頭和傳感器捕捉作物密度信息和果園特征。收集的數(shù)據(jù)包括地形數(shù)據(jù)，如經(jīng)度、緯度和高度，以及從多個相機角度拍攝的3D圖像。每次掃描都會拍攝數(shù)千張照片，然后使用AI深度學習引擎實時處理，以識別芽、芽、花、果實和樹冠特征。為此，F(xiàn)ruitometric使用內(nèi)部處理集群，而不是依賴于緩慢而昂貴的基于云的處理和存儲服務。
Since 2020 Fruitometry has scanned over 3000 hectares of kiwifruit, detected 1.2 billion kiwifruit features, and processed 65 million commercial kiwifruit images. 2023 harvest accuracy results gathered in May were 96.1% on the first set of 21 data points.
自2020年以來，F(xiàn)ruitometry已經(jīng)掃描了3000多公頃的獼猴桃，檢測了12億個獼猴桃特征，并處理了6500萬張商業(yè)獼猴桃圖像。5月份收集的2023年收獲準確率結(jié)果在第一組21個數(shù)據(jù)點上為96.1%。
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