阅读打卡营第三天

第一打卡点： 速记第三组核心词，15个，大约 3-5 分钟完成

四级核心词

在FastPass速通-单词练习中，完成“四级核心词汇-组3”

六级核心词

在FastPass速通-单词练习中，完成“六级核心词汇-组3”

第二打卡点： 阅读教案+观看方法讲解视频，大约 20-30 分钟完成

长篇阅读

一、解题步骤

长篇阅读侧重考查考生的段落信息匹配能力，考生可参照以下步骤进行解题。

• 步骤一：看标题
  • 目的：标题词往往是文章的主题词，文中会反复出现，有助于理解文章大意。
• 步骤二：先题后文
  • 目的：找到2-3个词或短语，这些词应独一无二且不易被改写，便于回文定位。
  • 定位词类型：
    • 专有名词、数词、最高级、标点符号
    • 名词或名词短语（特别是专业概念）
    • 名词+动词结构
• 步骤三：回文定位
  • 目的：题目的答案在文中是按顺序出现的，在对应的段落寻找定位词。
    • 第一遍：聚焦段首、段尾和转折词（如 however, but 等）所在位置。
    • 第二遍：针对剩余部分仔细比对，确保理解无误。

二、方法讲解

三、例题解析

The Challenges for Artificial Intelligence in Agriculture

[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.

[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.

[C] In this instance, the farmers and agronomist are looking to 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.

[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.

[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 dataset, 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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[O] Practically, to say that AI 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.

[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.

[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, skill, and funding are needed to test these technologies in farmers' fields.

[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.

36. Farmers will not profit from replanting once they have applied most of the fertilizer and other chemicals to their fields.

37. Agriculture differs from the medical science of the human body in that its environment is not a contained one.

38. The agronomist is sure that he will obtain a near accurate count of plant population with his software.

39. The application of artificial intelligence to agriculture is much more challenging than to most other industries.

40. Even the farmers know the data provided by the UAV is not correct.

41. The pressure for quick results leads to product failure, which, in turn, arouses doubts about the applicability of AI technology to agriculture.

42. Remote sensors are aimed to help farmers improve decision-making to increase yields.

43. The farmer expects the software to tell him whether he will have to replant any parts of his farm fields.

44. Agriculture proves very difficult to quantify because of the constantly changing conditions involved.

45. The same seed and fertilizer program may yield completely different outcomes in different places.

答案及解析：

标题中的 "Artificial Intelligence" 和 "Agriculture" 可能会在文中反复出现，是这篇文章的核心主题，解题时需关注与AI技术和农业相关的内容。

36. D

题目：Farmers will not profit from replanting once they have applied most of the fertilizer and other chemicals to their fields.

定位词： "profit from replanting" "fertilizer and chemicals"

解析：在段落[D]，提到“Once these have been applied, it becomes economically unviable to take corrective action”，说明施肥和使用化学品之后，补种不再有经济效益。因此，答案为 D 段。

37. L

题目：Agriculture differs from the medical science of the human body in that its environment is not a contained one.

定位词： "not a contained one" "agriculture" "medical science"

解析：段落[L]提到农业不像人体那样是一个封闭环境，农业处在大自然中，是一个复杂的生态系统。因此，答案为 L 段。

38. E

题目：The agronomist is sure that he will obtain a near accurate count of plant population with his software.

定位词： "near accurate count" "software"

解析：段落[E]提到，农学家认为软件会给出接近准确的结果，因此他充满信心。因此，答案为 E 段。

39. N

题目：The application of artificial intelligence to agriculture is much more challenging than to most other industries.

定位词： "AI" "more challenging" "agriculture"

解析：在段落[N]提到，农业中的AI应用比其他行业更具挑战性，因为农业环境不封闭，变化复杂。因此，答案为 N 段。

40. F

题目：Even the farmers know the data provided by the UAV is not correct.

定位词： "farmers" "UAV" "not correct"

解析：段落[F]提到，即使是对远程感应图理解有限的农民，也能看出数据不正确。因此，答案为 F 段。

41. Q

题目：The pressure for quick results leads to product failure, which, in turn, arouses doubts about the applicability of AI technology to agriculture.

定位词： "quick results" "product failure" "doubts"

解析：段落[Q]提到，快速推向市场的压力导致产品失败，进而引发对AI技术适用性的质疑。因此，答案为 Q 段。

42. H

题目：Remote sensors are aimed to help farmers improve decision-making to increase yields.

定位词： "Remote sensors" "improve decision-making" "increase yields"

解析：段落[H]提到，遥感器帮助农民做出决策，提高产量，因此答案为 H 段。

43. C

题目：The farmer expects the software to tell him whether he will have to replant any parts of his farm fields.

定位词： "software" "replant"

解析：段落[C]提到，农民使用软件确定是否需要重新播种作物。因此，答案为 C 段。

44. K

题目：Agriculture proves very difficult to quantify because of the constantly changing conditions involved.

定位词： "difficult to quantify" "changing conditions"

解析：段落[K]提到农业环境变化多端，难以量化。因此，答案为 K 段。

45. M

题目：The same seed and fertilizer program may yield completely different outcomes in different places.

定位词： "same seed and fertilizer" "different outcomes"

解析：段落[M]提到，同样的种子和肥料计划在不同地方可能有不同的结果。因此，答案为 M 段。

第三打卡点： 真题练习，尽量 10-15 分钟左右完成

四级阅读真题：23上①-Section B

六级阅读真题：23上①-Section B