植物育種學家每次會栽培數(shù)千個潛力品種；直到現(xiàn)在，對植物關(guān)鍵特征的觀察都是人工完成的。在一項新的研究中，在對潛力品種的測試里，無人駕駛飛行器，或無人駕駛飛機，可成功地用來遠程評估和預測大豆成熟時間。使用無人機來完成這項工作可以大大減少評估新作物所需的工時。
當植物育種學家開發(fā)新的作物品種時，他們會種植很多植物，而且他們都需要反復檢查。
“農(nóng)民可能會有100英畝土地，只種植一個大豆品種，而植物育種學家可能會在10英畝土地上種植1萬種潛在品種。農(nóng)民可以快速地確定田地里的單一大豆品種什么時候才能收割。但是，在秋天，植物育種學家必須反復走過實驗田，以確定每種潛在作物的成熟時間，” 伊利諾伊大學大豆育種家布瑞恩 迪爾思解釋說。
“我們每三天都必須進行檢查，”碩士生內(nèi)森 施米茨補充道。“在一年中的收獲季節(jié)里，這要花費我們大量的時間。而且田地里有時候很熱，有時候又很泥濘?！?/p>
為了簡化工作，一個跨學科的研究團隊，包括植物育種學家，計算機科學家，工程師和地理信息專家都轉(zhuǎn)向無人駕駛飛行器——俗稱無人機領(lǐng)域的研究。
“當無人機能夠為我們所用，我們將研究如何才能將這項新技術(shù)應(yīng)用到育種領(lǐng)域。這是首次嘗試，我們試圖把復雜的事情簡單化，”迪爾斯說。
其中一個目標是，利用裝載在無人機上的攝像頭，以及復雜的數(shù)據(jù)和成像分析技術(shù)，預測蠶豆的成熟時間?！拔覀兝枚喙庾V成像技術(shù)，”施米茨解釋說。“我們在程序中建立一個方程式，以便獲取反射在植物上的光頻變化。顏色的變化就是我們?nèi)绾螌⒊墒炫c不成熟植物區(qū)分開的依據(jù)?！?/p>
研究人員開發(fā)了一種算法，將無人機獲取的圖像與用傳統(tǒng)方法（通過田間研究）衡量的蠶豆成熟度數(shù)據(jù)進行對比。我們用無人機進行的成熟度預測非常接近我們田間研究的記錄，迪爾斯指出。
通過模型做出的預測準確率達到93%，但是，迪爾斯說，如果沒有無人機自身固有的局限性，他們可能會做的更好。例如，無人機只能在陽光明媚和風力較小的日子里飛行。
對于它們在提高農(nóng)業(yè)領(lǐng)域的效率和準確率方面，無人機得到了越來越多的認可，尤其是2016年8月新的FAA（聯(lián)邦航空局）規(guī)則生效后，本研究是首批利用無人機優(yōu)化育種實踐的研究。迪爾斯指出，該應(yīng)用對于大型育種企業(yè)非常實用，它們每年要測試數(shù)十萬個潛在品種。如果利用這項技術(shù)，能夠讓植物育種學家節(jié)省時間和精力，新品種就可以被更快地開發(fā)出來供農(nóng)民使用，這是一個受歡迎的改進。
論文，“基于無人機平臺，提升大豆估產(chǎn)方法和植物成熟度預測的開發(fā)方法”已經(jīng)發(fā)表在《環(huán)境遙感》期刊上。除了迪爾斯和施米茨，Neil Yu, Liujun Li, Lei Tian, 和 Jonathan Greenberg也是該論文的共同作者，他們都來自伊利諾伊大學。(張微編譯)
以下為英文原文：
Drones are what's next for plant breeders
Crop breeders grow thousands of potential varieties at a time; until now, observations of key traits were made by hand. In a new study, unmanned aerial vehicles, or drones, were used successfully to remotely evaluate and predict soybean maturity timing in tests of potential varieties. The use of drones for this purpose could substantially reduce the man-hours needed to evaluate new crops.
When plant breeders develop new crop varieties, they grow up a lot of plants and they all need to be checked. Repeatedly.
"Farmers might have a 100-acre field planted with one soybean variety, whereas breeders may have 10,000 potential varieties planted on one 10-acre field. The farmer can fairly quickly determine whether the single variety in a field is ready to be harvested. However, breeders have to walk through research fields several times in the fall to determine the date when each potential variety matures," explains University of Illinois soybean breeder Brian Diers.
"We have to check every three days," masters student Nathan Schmitz adds. "It takes a good amount of time during a busy part of the year. Sometimes it's really hot, sometimes really muddy."
To make things easier, an interdisciplinary team including breeders, computer scientists, engineers, and geographic information specialists turned to unmanned aerial vehicles – commonly known as UAVs or drones.
"When drones became available, we asked ourselves how we could apply this new technology to breeding. For this first attempt, we tried to do a couple simple things," Diers says.
One goal was to predict the timing of pod maturity using images from a camera attached to the drone, along with sophisticated data and image analysis techniques. "We used multi-spectral images," Schmitz explains. "We set up an equation in the program to pick up changes in the light frequency reflected off the plant. That color change is how we differentiate a mature plant from an immature one."
The researchers developed an algorithm to compare images from the drone with pod maturity data measured the old-fashioned way, by walking the fields. "Our maturity predictions with the drone were very close to what we recorded while walking through the fields," Diers notes.
Predictions made by the model achieved 93 percent accuracy, but Diers says they might have done even better without some of the inherent limitations of flying drones. For example, they could only fly it and obtain good images on sunny days with little wind.
Drones are increasingly recognized for their potential to improve efficiency and precision in agriculture—especially after new FAA rules went into effect in August 2016—but this is one of the first studies to use drones to optimize breeding practices. Diers notes that the application could be particularly useful to large breeding companies, which test hundreds of thousands of potential varieties annually. If breeders can save time and effort using this technology, new varieties could potentially be developed and made available to farmers on a faster timeline—a welcome improvement.
The article, "Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform," is published in Remote Sensing of Environment. In addition to Diers and Schmitz, Neil Yu, Liujun Li, Lei Tian, and Jonathan Greenberg, all from the University of Illinois, are co-authors.
來源: 中國科技網(wǎng) 作者: 張微編譯
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