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人类/人性(Humanity)正在将它本身推向危机。 我们人类所属的物种——智人(Homo sapiens) 的种群数量和分布,像吹气球一样在地球上膨胀开来,预计2050年会达到100亿—但是地球并不会莫名其妙随着长大,越来越多的人类不得不挤满在大小不变的地面上。再加上全球变暖和随之引发的水源短缺问题,人类看来真的要喂不饱自己了。

也许在不经意之间,真正智能的,真正让人感到大有可为的机器以及机器学习算法已经出现。它们也许会帮助掀起绿色革命<Green Revolution> ,养活这颗蓝色星球上不断增加的人类。这些新出现的机器、算法(程序)包括自动探测干旱发生规律的卫星,自动监测作物、清理病株的拖拉机, 能够帮助农民鉴别作物病虫害的、应用人工智能技术的手机app。

第 1 段(可获 2 积分)

未来的农业时代需要机器辅助,稻草人驱鸟的做法已经显得陈旧了,忘了这些旧办法吧。

数字“绿”手指

(像金手指辅助游戏一样,数字绿手指辅助农业)<A Digital Green Thumb>

深度学习是一种强大的计算机进行计算的方式。进行深度学习开发的程序员不是像传统编程那样编写指令告诉计算机“要做什么”,而是给出模式,让计算机自己从模式中总结该如何进行计算。(不是主动编程输入染病的植株有什么特征,而是)可以将正常的和染病的植株照片倒入电脑,告诉电脑哪些照片对应染病植株,电脑会自动学会/提取正常植株和染病植株的特征,之后就可以判定别的植株是否正常了。

生物学家 David Hughes 和 流行病学家 Marcel Salathé  用这种方法对被26种病害感染的14 种作物进行了分析处理。他们将超过5万张照片导入计算机,计算机程序在自主学习之后,对输入的新照片的识别正确率达到了99.35%

第 2 段(可获 2 积分)

然而,这些图片对光线和背景进行了均一化的预先处理,所以计算机检测和判断植株叶片(相对于实拍的来说)相当轻松。计算机分析从网上下载的植株照片的准确度仅有30%-40%。

虽然他们的工作结果不差,(但是他们希望更进一步,) Hughes and Salathé 希望这个AI能够驱动他们的App: PlantVillage。PlantVilliage目前(仅仅)能让来自世界各地的农民 上传它们状态欠佳的作物的照片 到论坛上,给专家来分析。为了让AI更智能,他们计划继续输入更多的受到病害的植株照片。“包括更多的不同来源的照片,这些照片根据拍照方式、一年中拍摄的时间、拍摄地区等等进行(更细化的)分类,” Salathé说,“算法会自动识别这些分类并学习。”

第 3 段(可获 2 积分)

植物长势欠佳有很多种原因,不能总是想着寻找感染源。“绝大部分阻碍收成的病害由物理因素/压力引发,比如钙镁缺乏或者盐和热量超标,” Hughes说。 “人们常常认为这些病害是由细菌或者真菌引发的。” 对病害的误判会让农民工在农药或者除草剂上费钱费时费力不讨好。未来,AI能够帮助农民快速、精准地指出问题。

即使问题的原因找到了,人们也只是扳回一局而已—就算app能够定位问题是什么, 还是得请来经验丰富的专家才能针对气候、土壤、季节给出一个解决方案。联合国粮农组织认为这些技术对于作物种植的管理只算是“有用的工具”,还是应该以专家给出的专业指导为主。 因此,Fazil Dusunceli, 一个和联合国粮农组织一起的植物病理学家说,给人们提供这些电子建议当然是一件好事,但是“最终的害虫管理决策需要和到场的专家共同商讨制定。”

第 4 段(可获 2 积分)

Tractor Trainer

While the developing world is hungry for agricultural knowledge, the developed world is drowning in pesticides and herbicides. In the US each year, farmers use 310 million pounds of herbicide—on just corn, soy, and cotton fields. It’s the spray-and-pray approach, not so much sniping as carpet bombing.

A company called Blue River Technology may have hit upon solution, at least as far as lettuce is concerned. Its LettuceBot looks like your typical tractor, but in fact it’s a machine-learning-powered … machine.

Blue River claims the LettuceBot can roll through a field photographing 5,000 young plants a minute, using algorithms and machine vision to identify each sprout as lettuce or a weed. If that seems too impossibly fast to you, “it’s well within the computing of machine learning and computer vision,” says Jeremy Howard, founder of deep-learning outfit Enlitic. A graphics chip can identify an image in just .02 seconds, he adds.

第 5 段(可获 2 积分)

With an accuracy within a quarter inch, the bot pinpoints and sprays each weed on the fly. If it eyeballs a lettuce plant and determines it isn’t growing optimally, it’ll spray that too (farmers overplant lettuce by a factor of five, so they can sacrifice plenty of extras). If two sprouts ended up too close to one another during planting (not ideal), the machine can discern them from, say, one particularly large plant, and zap them as well.

Now, consider the alternative: spraying a field with herbicides willy-nilly. “It’s akin to saying if a few people in the city of San Francisco had an infection, your only solution would be to give every man woman, and child in the city an antibiotic,” says Ben Chostner of Blue River Technology. “People would be cured, but it’s expensive, it’s not using the antibiotics to the best of their potential.”

第 6 段(可获 2 积分)

With the LettuceBot, on the other hand, Chostner says farmers can reduce their use of chemicals by 90 percent. And the machine is already hard at work—Blue River treats fields that supply 10 percent of the lettuce in the US annually.

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LettuceBot2.jpg

Blue River Technology’s LettuceBot can photograph and treat 5,000 plants a minute. Blue River Technology

LettuceBot is so powerful because it uses machine learning to make one of the few things robots are already great at even better: precision. Robots can’t run like us or manipulate objects quite like we do, but they’re consistent and meticulous—the perfect agricultural snipers.

第 7 段(可获 2 积分)

Life From Above

Orbiting over 400 miles above your head, NASA’s Landsat satellites provide a downright magical survey of Earth’s surface in a slew of bandwidths far beyond the visible spectrum. All of these layers of information are hard to digest for a human, to be sure, but for machine learning algorithms, they ain’t no thing.

And that could be extremely valuable for monitoring agriculture, particularly in developing countries, where governments and banks face a dearth of data when making decisions about which farmers they give loans or emergency assistance to. During a drought in India, for instance, not only will regions suffer to different degrees, but within those regions some farmers might have better means to procure water than others.

第 8 段(可获 2 积分)

So a startup called Harvesting is analyzing satellite data on a vast scale with machine learning, with the idea to help institutions distribute money more efficiently. “Our hope is that in using this technology we would be able to segregate such farmers and villages and have banks or governments move dollars to the right set of people,” says Harvesting CEO Ruchit Garg. While a human analyst can handle 10, maybe 15 variables at a time, Garg says, machine learning algorithms can handle 2,000 or more. That’s some serious context.

Choosing where to allocate resources is a particularly pressing problem for governments as a warming Earth sends the climate into chaos. Traditionally, farming in India has been a relatively predictable affair, at least as far as humans holding dominion over their environment goes. “So what I learned from my father, my grandfather, that’s how I grow, these are the seasons I know,” Garg says. “However because of drastic climate change, things are no longer what my father or my grandfather used to do.”

It’s the new world order, folks. Farmers can take the punches, or they can farm smarter. More data, more AI, and more chemical-spraying robots.

As for those tomato plants you keep neglecting—that one’s on you, I’m afraid.

第 9 段(可获 2 积分)

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