Section B

Directions: In this section, you are going to read a passage with ten statements attached to it. Each

statement contains information given in one of the paragraphs. Identify the paragraph from which the

information is derived. You may choose a paragraph more than once. Each paragraph is marked with a

letter. Answer the questions by marking the corresponding letter on Answer Sheet 2.

The Challenges for Artificial Intelligence in Agriculture

A) A group of com farmers stands huddled around an agronomist 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 com 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 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.

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  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 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. .

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, skills, and funding is 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.

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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 Al 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.