Enabling precision agriculture with AI - Microsoft's FarmBeats Program

My name is Shawn Stratman, and I'm a farmer.
我的名字叫Shawn Stratman，我是一位農夫。

I've been doing this a long time and it's clear now more than ever that we need to find better ways of growing to ensure a sustainable future for farming.
我已經做這一行很久了。這讓我更知道，我們必須找到一個方法，在未來可以永續耕作。

Water scarcity and agricultural pollution are all creating pressures that farmers around the world are feeling.
水源的減少和農業的污染，令全世界的農夫感到壓力。

We are looking to human ingenuity and technology to feed our growing population.
我們希望人類的聰明才智與科技，可以有足夠糧食給成長的人口。

My background is in connectivity and battery technologies, but I grew up spending summers on our family farm in India.
我是學電池相關科學的，但是我從小暑假都與我家人在印度的農場。

That foundation gives me a unique perspective in my work, as a principal researcher at Microsoft.
這對於我在微軟當研究員是一個特別的基底。

New sensor technology is a great tool in providing data on soil moisture and soil nutrients to help farmers make best use of their resources.
新的感測機技術能提供關於土壤濕度、營養的數據，幫助農夫運用資源。

Unfortunately, these sensor solutions can cost thousands of dollars first sensor, and requires many sensors to monitor appeals properly.
不幸的，這些感測機需耗費上千美金，還需要跟多感測機來準確控制。

Drones and other imaging technologies can provide very useful data and allow for precise monitoring of land over time.
無人駕駛飛機和其他想像中的新科技可以提供實用的資訊，來更精準控制土壤狀況。

But these devices require a high bandwidth wireless connection from the middle of the farm.
但這些設備需要在農場中間有高頻寬無限連線。

To solve our connectivity problem, we utilize the unused channels for TV broadcasts, which are abundant in rural areas.
為了解決連線的問題，我們運用了鄉下很多、且沒在使用的電視廣播。

This allows us to transfer large amounts of data from cameras and sensors in these remote areas to a local processor at the farm and out to the Microsoft cloud.
這讓我們可以傳輸大量數據。從相機和感測器，傳送到農場的處理機或是微軟雲端。

So, I send this up, and it's taking pictures all the time.
所以我把這個放到空中，他會持續照相。

Our big breakthrough was the integration of machine learning into the processing of imaging and ground sensor data.
我們最大的突破，是把機器人學習結合在圖像處理與地上感測機數據。

Through this process, the data insights are magnified.
利用這個過程，數據可以顯而易見。

From two finite data points, we are able to create a detailed heat map of the farm.
運用兩個有限的數據點，我們可以建立一個詳細的農場溫度地圖。

The farmer is able to access this map and utilize it to inform resource management, and to track the effects of the inputs.
農夫可以通過這個地圖來規劃資源利用，和追蹤成效。

Having a greater understanding of soil conditions and how our actions affect them, it allows us to put the work in where it's needed.
有效了解土壤狀況和我們會怎麼影響它，可以讓我們更好分配土壤運用。

It's changing the way that we work with the land, and it's changing the way the land works for us.
這改變了我們怎麼對土壤，也改變了土讓怎麼對我們。

Copyright Announcement: The above videos are  from the official Youtube channel of the organization. Enabling precision agriculture with AI - Microsoft's FarmBeats Program https://www.youtube.com/channel/UCA14v8Qs71IJoqM1tcikzCg . The videos we selected are all publicly available on the official channel, and we do not own the copyright of these videos. Our work on Chinese translation for this film is free for the 1.2 billion audiences in Mandarin Chinese around the world to watch and learn SDGs and SROI advertising campaigns.