2020年12月六级第1套长篇阅读匹配原文翻译及答案

A) A group of corn farmers stands huddled around an agronomist (农学家) and his computer on the side of an irrigation machine in central South Africa. The agronomist has just flown over the field with a hybrid unmanned aerial vehicle (UAV) that takes off and lands using propellers yet maintains distance and speed for scanning vast hectares of land through the use of its fixed wings.

一群玉米种植者聚集在南非中部一台灌溉机旁的农学家和他的电脑周围。农学家刚刚使用一架混合式无人机飞越田地，该无人机通过螺旋桨起降，但利用固定翼保持距离和速度以扫描广阔的公顷土地。

B) The UAV is fitted with a four spectral band precision sensor that conducts onboard processing immediately after the flight, allowing farmers and field staff to address, almost immediately, any crop abnormalities that the sensor may have recorded, making the data collection truly real-time.

该无人机配备了一个四光谱波段精密传感器，可在飞行后立即进行机载处理，使农民和现场工作人员能够几乎立即处理传感器可能记录到的任何作物异常，使数据收集真正实时。

C) In this instance, the farmers and agronomists are looking for specialized software to give them an accurate plant population count. It's been 10 days since the corn emerged and the farmer wants to determine if there are any parts of the field that require replanting due to a lack of emergence or wind damage, which can be severe in the early stages of the summer rainy season.

在这种情况下，农民和农学家正在寻找专门的软件来提供准确的植物种群计数。玉米出苗已有10天，农民想要确定田地是否有任何部分因出苗不足或风害需要补种，风害在夏季雨季初期可能很严重。

D) At this growth stage of the plant's development, the farmer has another 10 days to conduct any replanting before the majority of his fertilizer and chemical applications need to occur. Once these have been applied, it becomes economically unviable to take corrective action, making any further collected data historical and useful only to inform future practices for the season to come.

在植物生长的这一阶段，农民还有10天时间进行任何补种，然后大部分肥料和化学药剂需要施用。一旦施用后，采取纠正措施在经济上变得不可行，使得任何进一步收集的数据成为历史数据，仅可用于指导未来的季节实践。

E) The software completes its processing in under 15 minutes producing a plant population count map. It's difficult to grasp just how impressive this is, without understanding that just over a year ago it would have taken three to five days to process the exact same data set, illustrating the advancements that have been achieved in precision agriculture and remote sensing in recent years. With the software having been developed in the United States on the same variety of crops in seemingly similar conditions, the agronomist feels confident that the software will produce a near accurate result.

该软件在不到15分钟内完成处理，生成植物种群计数地图。如果不了解仅仅一年多前处理完全相同的数据集需要三到五天，就很难理解这有多令人印象深刻，这说明了近年来在精准农业和遥感方面取得的进步。由于该软件是在美国针对类似条件下相同作物品种开发的，农学家相信软件会产生接近准确的结果。

F) As the map appears on the screen, the agronomist's face begins to drop. Having walked through the planted rows before the flight to gain a physical understanding of the situation on the ground, he knows the instant he sees the data on his screen that the plant count is not correct, and so do the farmers, even with their limited understanding of how to read remote sensing maps.

当地图出现在屏幕上时，农学家的脸色开始沉下来。在飞行前，他已经走过种植行，以实地了解情况，因此他一看到屏幕上的数据就知道植物计数不正确，农民们也是如此，尽管他们对如何解读遥感地图的了解有限。

G) Hypothetically, it is possible for machines to learn to solve any problem on earth relating to the physical interaction of all things within a defined or contained environment by using artificial intelligence and machine learning.

假设上，通过使用人工智能和机器学习，机器有可能学习解决地球上任何与在界定或封闭环境中所有事物物理交互相关的问题。

H) Remote sensors enable algorithms (算法) to interpret a field's environment as statistical data that can be understood and useful to farmers for decision-making. Algorithms process the data, adapting and learning based on the data received. The more inputs and statistical information collected, the better the algorithm will be at predicting a range of outcomes. And the aim is that farmers can use this artificial intelligence to achieve their goal of a better harvest through making better decisions in the field.

远程传感器使算法能够将田地环境解读为统计数据，这些数据对农民来说是可理解且有助于决策的。算法处理数据，根据接收到的数据进行适应和学习。收集的输入和统计信息越多，算法预测一系列结果的能力就越强。目标是农民可以利用这种人工智能通过更好地在田间决策来实现更好的收成。

I) In 2011, IBM, through its R&D Headquarters in Haifa, Israel, launched an agricultural cloud computing project. The project, in collaboration with a number of specialized IT and agricultural partners, had one goal in mind—to take a variety of academic and physical data sources from an agricultural environment and turn these into automatic predictive solutions for farmers that would assist them in making real-time decisions in the field.

2011年，IBM通过其位于以色列海法的研发总部启动了一个农业云计算项目。该项目与多个专业IT和农业合作伙伴合作，有一个目标：从农业环境中获取各种学术和物理数据源，并将这些转化为自动预测解决方案，帮助农民在田间进行实时决策。

J) Interviews with some of the IBM project team members at the time revealed that the team believed it was entirely possible to "algorithm" agriculture, meaning that algorithms could solve any problem in the world. Earlier that year, IBM's cognitive learning system, Watson, competed in the game Jeopardy against former winners Brad Rutter and Ken Jennings with astonishing results. Several years later, Watson went on to produce ground-breaking achievements in the field of medicine.

当时对一些IBM项目团队成员的采访显示，团队相信完全有可能“算法化”农业，意思是算法可以解决世界上的任何问题。同年早些时候，IBM的认知学习系统Watson与前任冠军Brad Rutter和Ken Jennings在游戏《危险边缘》中竞争，取得了惊人成果。几年后，Watson在医学领域取得了突破性成就。

K) So why did the project have such success in medicine but not agriculture? Because it is one of the most difficult fields to contain for the purpose of statistical quantification. Even within a single field, conditions are always changing from one section to the next. There's unpredictable weather, changes in soil quality, and the ever-present possibility that pests and diseases may pay a visit. Growers may feel their prospects are good for an upcoming harvest, but until that day arrives, the outcome will always be uncertain.

那么，为什么该项目在医学上如此成功，但在农业上却没有呢？因为农业是最难为统计量化目的而控制的领域之一。即使在一个田地内，条件也总是从一个部分到另一个部分不断变化。有不可预测的天气、土壤质量的变化，以及病虫害随时可能发生的可能性。种植者可能觉得即将到来的收成前景良好，但在那天到来之前，结果总是不确定的。

L) By comparison, our bodies are a contained environment. Agriculture takes place in nature, among ecosystems of interacting organisms and activity, and crop production takes place within that ecosystem environment. But these ecosystems are not contained. They are subject to climatic occurrences such as weather systems, which impact upon hemispheres as a whole, and from continent to continent. Therefore, understanding how to manage an agricultural environment means taking literally many hundreds if not thousands of factors into account.

相比之下，我们的身体是一个封闭环境。农业发生在自然界中，在相互作用的生物体和活动的生态系统中，作物生产发生在这种生态系统环境中。但这些生态系统不是封闭的。它们受到气候事件的影响，如天气系统，这些系统影响整个半球，甚至跨越大陆。因此，理解如何管理农业环境意味着要考虑到数百甚至数千个因素。

M) What may occur with the same seed and fertilizer program in the United States' Midwest region is almost certainly unrelated to what may occur with the same seed and fertilizer program in Australia or South Africa. A few factors that could impact on variation would typically include the measurement of rain per unit of a crop planted, soil type, patterns of soil degradation, daylight hours, temperature and so forth.

在美国中西部地区使用相同种子和肥料计划可能发生的情况，几乎肯定与在澳大利亚或南非使用相同种子和肥料计划可能发生的情况无关。一些可能影响变化的因素通常包括每单位种植作物的降雨量测量、土壤类型、土壤退化模式、日照时间、温度等。

N) So the problem with deploying machine learning and artificial intelligence in agriculture is not that scientists lack the capacity to develop programs and protocols to begin to address the biggest of growers' concerns; the problem is that in most cases, no two environments will be exactly alike, which makes the testing, validation and successful rollout of such technologies much more laborious than in most other industries.

因此，在农业中部署机器学习和人工智能的问题不在于科学家缺乏开发程序和协议以开始解决种植者最大关切的能力；问题在于，在大多数情况下，没有两个环境是完全相同的，这使得这些技术的测试、验证和成功推广比大多数其他行业更加费力。

O) Practically, to say that Al and Machine Learning can be developed to solve all problems related to our physical environment is to basically say that we have a complete understanding of all aspects of the interaction of physical or material activity on the planet. After all, it is only through our understanding of 'the nature of things' that protocols and processes are designed for the rational capabilities of cognitive systems to take place. And, although Al and Machine Learning are teaching us many things about how to understand our environment, we are still far from being able to predict critical outcomes in fields like agriculture purely through the cognitive ability of machines.

实际上，说人工智能和机器学习可以被开发来解决与我们物理环境相关的所有问题，基本上是在说我们对地球上物理或物质活动交互的所有方面有完全的理解。毕竟，只有通过我们对‘事物本质’的理解，才能设计协议和流程，让认知系统的合理能力得以发挥。而且，尽管人工智能和机器学习正在教我们许多关于如何理解环境的知识，但我们仍然远不能纯粹通过机器的认知能力来预测像农业这样的领域的关键结果。

P) Backed by the venture capital community, which is now investing billions of dollars in the sector, most agricultural technology startups today are pushed to complete development as quickly as possible and then encouraged to flood the market as quickly as possible with their products.

在风险投资界的支持下，目前该行业投资数十亿美元，今天大多数农业科技初创公司被推动尽快完成开发，然后鼓励尽快将产品推向市场。

Q) This usually results in a failure of a product, which leads to skepticism from the market and delivers a blow to the integrity of Machine Learning technology. In most cases, the problem is not that the technology does not work, the problem is that industry has not taken the time to respect that agriculture is one of the most uncontained environments to manage. For technology to truly make an impact on agriculture, more effort, skills, and funding is needed to test these technologies in farmers' fields.

这通常导致产品失败，引起市场的怀疑，并打击了机器学习技术的完整性。在大多数情况下，问题不在于技术不起作用，而在于行业没有花时间尊重农业是最不受控制的环境之一。要让技术真正对农业产生影响，需要更多的努力、技能和资金在农民的田间测试这些技术。

R) There is huge potential for artificial intelligence and machine learning to revolutionize agriculture by integrating these technologies into critical markets on a global scale. Only then can it make a difference to the grower, where it really counts.

通过将人工智能和机器学习整合到全球关键市场中，这些技术有巨大潜力彻底改变农业。只有这样，才能对种植者产生影响，在真正重要的地方。