植物育种学家每次会栽培数千个潜力品种；直到现在，对植物关键特征的观察都是人工完成的。在一项新的研究中，在对潜力品种的测试里，无人驾驶飞行器，或无人驾驶飞机，可成功地用来远程评估和预测大豆成熟时间。使用无人机来完成这项工作可以大大减少评估新作物所需的工时。
当植物育种学家开发新的作物品种时，他们会种植很多植物，而且他们都需要反复检查。
“农民可能会有100英亩土地，只种植一个大豆品种，而植物育种学家可能会在10英亩土地上种植1万种潜在品种。农民可以快速地确定田地里的单一大豆品种什么时候才能收割。但是，在秋天，植物育种学家必须反复走过实验田，以确定每种潜在作物的成熟时间，” 伊利诺伊大学大豆育种家布瑞恩 迪尔思解释说。
“我们每三天都必须进行检查，”硕士生内森 施米茨补充道。“在一年中的收获季节里，这要花费我们大量的时间。而且田地里有时候很热，有时候又很泥泞。”
为了简化工作，一个跨学科的研究团队，包括植物育种学家，计算机科学家，工程师和地理信息专家都转向无人驾驶飞行器——俗称无人机领域的研究。
“当无人机能够为我们所用，我们将研究如何才能将这项新技术应用到育种领域。这是首次尝试，我们试图把复杂的事情简单化，”迪尔斯说。
其中一个目标是，利用装载在无人机上的摄像头，以及复杂的数据和成像分析技术，预测蚕豆的成熟时间。“我们利用多光谱成像技术，”施米茨解释说。“我们在程序中建立一个方程式，以便获取反射在植物上的光频变化。颜色的变化就是我们如何将成熟与不成熟植物区分开的依据。”
研究人员开发了一种算法，将无人机获取的图像与用传统方法（通过田间研究）衡量的蚕豆成熟度数据进行对比。我们用无人机进行的成熟度预测非常接近我们田间研究的记录，迪尔斯指出。
通过模型做出的预测准确率达到93%，但是，迪尔斯说，如果没有无人机自身固有的局限性，他们可能会做的更好。例如，无人机只能在阳光明媚和风力较小的日子里飞行。
对于它们在提高农业领域的效率和准确率方面，无人机得到了越来越多的认可，尤其是2016年8月新的FAA（联邦航空局）规则生效后，本研究是首批利用无人机优化育种实践的研究。迪尔斯指出，该应用对于大型育种企业非常实用，它们每年要测试数十万个潜在品种。如果利用这项技术，能够让植物育种学家节省时间和精力，新品种就可以被更快地开发出来供农民使用，这是一个受欢迎的改进。
论文，“基于无人机平台，提升大豆估产方法和植物成熟度预测的开发方法”已经发表在《环境遥感》期刊上。除了迪尔斯和施米茨，Neil Yu, Liujun Li, Lei Tian, 和 Jonathan Greenberg也是该论文的共同作者，他们都来自伊利诺伊大学。(张微编译)
以下为英文原文：
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.
来源: 中国科技网 作者: 张微编译
