雅思阅读 66: The Drone That Reads the Field（读懂农田的无人机）

改编自 Frontiers in Agronomy / University of Maryland（2025年）。雅思阅读 Section 3 难度，约 1050 词。 素材来源：https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2025.1670380/full

Reading Passage

A. A century of industrial farming taught farmers to treat a field as a single, uniform space. Tractors spread the same seed, the same fertiliser and the same spray across every row, because nobody could afford to walk every acre and measure every plant. That assumption is now dissolving. Modern farms are dotted with soil sensors, guided by satellites and — most visibly — patrolled by small drones that fly low over the crops taking pictures no human scout could ever capture. The promise of what is called precision agriculture is simple but profound: a field is not one field but thousands of tiny zones, each with its own soil, moisture and disease pressure, and the farmer should intervene only where, when and how much each zone actually needs. Instead of drowning the whole field in chemicals, the new approach asks the land precisely what it wants. A soil sensor buried at root depth knows, hour by hour, whether the crop is thirsty; a satellite overhead knows whether its leaves are darkening with stress; a drone that flies at dawn combines both readings into a map a farmer can act on before lunch. The change is not merely technological; it reverses a hundred years of thinking about what a farm even is. Where the industrial farm treated uniformity as a virtue, the precision farm treats uniformity as an error to be corrected row by row.

B. The drone is the tool that makes this possible. Equipped with multispectral cameras, a small unmanned aircraft can photograph a field in light the human eye cannot see — near-infrared bands that reveal how vigorously a plant is photosynthesising, even before its leaves show the first visible signs of stress. Combined with satellite imagery and machine-learning algorithms, these flights turn raw pictures into maps that predict yield, locate pests seven to ten days before symptoms appear, and spot patches where the soil lacks a specific nutrient. Research reviewed in a 2025 Frontiers study shows that integrating drone and satellite data with machine learning cuts irrigation costs by roughly 20 to 25 percent and reduces nitrogen application by as much as 31 kilograms per hectare, without any loss of productivity. In one corn case study, a drone identified yellowing caused by zinc and sulphur deficiency, then automatically produced a map splitting the field into two treatment zones; a drone-mounted sprayer applied fertiliser only where it was needed, cutting overall use by more than a third.

C. The savings extend beyond chemicals. Traditionally, a scout walks a field once every week or two, judging the crop by eye from a handful of spots. A drone can survey an entire field in a single morning, repeatedly, and at a resolution fine enough to distinguish individual plants. Farmers report that digital scouting has cut the cost of field monitoring by 40 to 60 percent, because the slow, human walk across mud is replaced by an automated flight that never tires. Early detection matters here: catching a fungal infection in its first few days means a targeted spray that stops it spreading, rather than a panicked, blanket treatment later. Disease-detection models now exceed 95 percent accuracy in recognising common blights on tomatoes, wheat and grapes. For a grower, that difference is the difference between a controllable problem and a lost harvest. Over a whole season, the repeated flights also build a growing archive: a record of exactly which patches of a field lagged this year, so that seed choice and irrigation can be adjusted long before the next crop is sown.

D. Yet the technology raises questions that a spreadsheet cannot answer. The first is cost. A drone, a multispectral camera, a subscription to a data platform and the software to interpret it all sit beyond the reach of the smallest farmers in the poorest regions, where the land is least efficient but the capital is scarcest. Precision agriculture, for all its environmental promise, could widen the gap between the large, data-rich farm and the smallholder who still judges the soil by hand. The second question is data itself. A drone that maps every field, and a platform that stores the maps, creates a record of almost everything a farmer grows, when and where. Who owns that data, and who might later use it to negotiate seed prices or insurance, is largely untested law. Finally, the models are only as good as the climate they were trained on; a new drought, a new pest or an unusual season can push the system into predictions no algorithm has seen. When the data also flows to distant software companies rather than staying on the farm, a farmer may find that decisions about his own land are being shaped by actors he has never met.

E. None of these objections has stopped the trend. The market for AI in precision agriculture is projected to grow fivefold within a decade, and the combination of cheaper drones, free satellite data and open-source machine learning is steadily lowering the entry cost. The most plausible future is not that machines replace the farmer but that the most experienced ones become conductors of a fleet of eyes in the sky — deciding where to walk, where to spray and when to harvest, guided by a map that updates itself every flight. The vision of a field treated as thousands of individual zones is finally within reach. Whether that promise is shared by every farmer on Earth, or only by the large ones who can afford it, will decide whether precision agriculture is an environmental revolution or simply another advantage for the already powerful. If the cost of sensors and software keeps falling, the same tools that now benefit the largest operations may eventually reach the smallholders who most need to use scarce water and fertiliser wisely.

Questions 1-4

Choose the correct heading for paragraphs B, C, D and E from the list of headings below.

List of Headings i. How drones turn invisible light into useful maps ii. The human cost of industrial farming iii. Saving money by replacing slow scouting iv. Questions of cost, data and trust v. Where the technology is heading vi. Why satellites have failed vii. The history of the tractor

1. Paragraph B: ____
2. Paragraph C: ____
3. Paragraph D: ____
4. Paragraph E: ____

Questions 5-8

Choose the correct letter, A, B, C or D.

5. What is the core idea of precision agriculture? A. Treating the whole field identically. B. Intervening only where and when each small zone needs it. C. Abandoning fertilisers completely. D. Farming without any human input.

6. Why can drones detect stress before the human eye? A. They fly higher than satellites. B. They capture near-infrared light that reveals photosynthetic vigour. C. They use chemical sensors in the soil. D. They spray fluorescent dye.

7. What did the corn case study demonstrate? A. Fertiliser use increased by a third. B. A drone mapped nutrient deficiency and targeted only the affected zones. C. The harvest was lost despite treatment. D. Manual scouting was faster than drones.

8. Why might precision agriculture widen inequality? A. Drones are banned in poor countries. B. Small farmers may be unable to afford the technology. C. It reduces yields for large farms. D. It uses more water than traditional methods.

Questions 9-13

Do the following statements agree with the claims of the writer?

Write:

• TRUE if the statement agrees with the information
• FALSE if the statement contradicts the information
• NOT GIVEN if there is no information on this

9. The Frontiers study found that machine learning can cut irrigation costs by roughly 20 to 25 percent.
10. Drone disease-detection models currently have an accuracy of under 50 percent.
11. Digital scouting has been reported to reduce monitoring costs by 40 to 60 percent.
12. The ownership of farm data is already fully regulated by international law.
13. Drones can be operated by farmers without any training.

Questions 14-15

Complete the summary below using NO MORE THAN TWO WORDS from the passage.

Multispectral cameras detect (14) ________ bands that reveal plant vigour, and when combined with machine learning, the resulting maps let farmers cut both water and (15) ________ use without reducing output.

答案与解析

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