現代農業(yè)智能改造傳統農業(yè)的14種方式【中英雙語】

隨著我們進入機器學習的新技術時代，人工智能和農業(yè)正變得如膠似漆。它帶來了令人興奮的無限可能性：從種子發(fā)芽，到保持作物的完整性，再到實際的收獲過程?？茖W家估計到2050年，全球人口將增加到97億人以上，那時很多饑餓的人口需要養(yǎng)活。相比于人口的大量增長，耕地面積只會增加4%。因此，解決辦法不是擴大農田來種植莊稼和飼養(yǎng)牲畜，而是更有效地利用現有的土地。回顧過去，我們看到大約70年前“綠色革命”的開始，它帶來了灌溉系統的改善，農田機械化的方法，以及新型的人造肥料。這些因素的疊加起來提高了糧食產量，全球約有10億人因此從饑餓中獲救。這種快速發(fā)展帶來了許多好處，如更高的產量，但也有許多負面因素：種植業(yè)大量使用殺蟲劑、化肥等激素，破壞了生物多樣性，一些不可或缺的生物滅絕。同時，那些耕作方法加在一起，向地球上的小溪和河流注入了大量的毒素，也耗盡了土壤的自然肥力。

As we enter the new technology era of machine learning, artificial intelligence and agriculture are becoming closely intertwined. It brings exciting infinite possibilities: from seed germination, to maintaining the integrity of the crop, to the actual harvesting process. Scientists estimate that the global population will grow to more than 9.7 billion people by 2050, when many hungry people will need to be fed. Compared to the massive population growth, the arable land area will only increase by 4 percent. Therefore, the solution is not to expand farmland to grow crops and raise livestock, but to moreeffectively use the existing land. Looking back, we see the start of the "Green Revolution" about 70 years ago, which brought improvements to irrigation systems, methods of mechanized farmland, and new types of man-made fertilizers. The combination of these factors has increased food production, and about a billion people worldwide have been saved from hunger. This rapid development has brought many benefits, such as higher yields, but there are also many negative factors: the heavy use of pesticides,fertilizers and other hormones, destroying biodiversity, and some indispensable biological extinction. At the same time, those farming methods put together inject large amounts of toxins into the earth's streams and rivers, as well as draining the soil's natural fertility.

可持續(xù)農業(yè)和糧食問題專家Danielle Nierenberg說：“這些方法從來沒打算長期使用?！比绻覀円^續(xù)保持糧食生產的穩(wěn)定和充足，就必須進行變革。目前，全球20%的人口受雇于農業(yè)綜合企業(yè)，這是一個價值3萬億美元的產業(yè)。但是我們如何進行這個變換呢？答案可以在人工智能和農業(yè)的融合發(fā)展中找到。新型的機器學習技術在農業(yè)各個解決方案中是如何推動生產的？人工智的大量應用，改善了發(fā)展中國家和已經領先的西方國家的農業(yè)狀況：

food production, we must change. Currently, 20% of the world's population is employed in an agribusiness, a $3 trillion industry. But how do we make this transformation? The answer can be found in the integrated development of artificial intelligence and agriculture. How do new machine-learning technologies drive production across agricultural solutions? The extensive application of artificial intelligence has improved the agricultural situation in developing countries and already leading Western countries:

1、人工智能選種

如果我們想要有最好的作物，那么這一切都取決于我們種植的種子的基因。Monsanto公司現在正在使用人工智能掃描具有最理想特性的種子的DNA序列。農民將不再需要投入時間和精力來進行種子的交叉變異實驗，因為現在有計算機程序可以為他們進行這種分析。種子本身有發(fā)芽率，或“種子休眠”，這意味著它們只有在特定條件下才會發(fā)芽和開始生長。研究人員可以利用人工智能找出種子發(fā)芽的最佳條件，如溫度和濕度水平，使作物能夠比預期的更早開始生長。這減少了等待時間，并可以使作物全年種植。機器學習支持的圖像分析的新應用，加上移動成像的自動化控制，可以測試種子的表型，以確定使用哪種種子最好。這方面的實例可以在種子發(fā)芽技術中找到，該技術已經用于測試番茄和玉米等作物。

1、Artificial intelligence for species selection

If we want to have the best crops, then it all depends on the genes of the seeds we grow. Monsanto Company is now using AI to scan the DNA sequences of seeds with the most desirable features. Farmers will no longer need to devote time and effort to cross-variation experiments on seeds, as there are now computer programs available to perform this analysis for them. The seeds themselves have a germination rate, or "seed dormancy," which means that they only germinate and start to grow under certain conditions. Researchers can use AI to find out the best conditions for seed germination, such as

2、通過人工智能反饋進行土壤管理

在世界各地種植農作物時，土壤營養(yǎng)也會發(fā)揮作用。通過特殊的算法，深度學習被帶到這里的最前沿，這些算法可以幫助監(jiān)測種植前和生長過程中土壤的健康狀況。

土壤退化和侵蝕也是影響農作物生長的重要因素，但這兩個問題都可以用人工智能解決，就像PEAT公司在德國做過的實驗那樣。他們開發(fā)了一種能分析土壤缺陷的Plantix。加上無人機的視覺感知能力，它們可以探測到作物的生長區(qū)域，這些作物可能生長在有缺陷的土壤中，或會遭受區(qū)域里疾病和害蟲的侵襲。

它通過對葉子成像，然后通過一個軟件運行，這個軟件可以區(qū)分正常和不健康的生長模式。更重要的是，軟件會向農民提出解決問題的方法。

CropDiagnosis是另一個類似的應用程序，它可以用無人機掃描整個領域，并且評估土壤中灌溉和氮含量水平。

在美國，Trace Genomics也在追隨他們的腳步，采用基于人工智能的技術來研究土壤弱點和作物缺陷。

2、Soil management through artificial intelligence feedback

Soil nutrition also plays a role when growing crops around the world. Deep learning is being brought here to the forefront of special algorithms that can help monitor the health of the soil before planting and during growth.

Soil degradation and erosion are also important factors affecting crop growth, but both problems can be solved with artificial intelligence, as PEAT did in Germany. They developed a Plantix that analyzes soil defects. Combined with the visual perception of drones, they can detect the growing areas of crops that may grow in defective soil or suffer fromdisease and pests in the areas.

It is run by imaging the leaves and then through a software that distinguishes between normal and unhealthy growth patterns. More importantly, the software presents farmers with solutions to the problem.

CropDiagnosis is another similar application that can scan entire fields with drones and assess irrigation and nitrogen levels in soil. In the US, Trace Genomics is also following in their footsteps, using AI-based technologies to study soil weaknesses and crop defects.

3、人工智能管理灌溉和用水

植物要想正常生長，就需要持續(xù)不斷的供給需要的水。在世界上雨水和淡水稀少或不可靠的地區(qū)，種植作物尤其困難。就像你的花園灑水器可以設置定時器一樣，現代的人工智能灌溉方法比這更進一步。

他們可以通過農業(yè)環(huán)境中的機器學習技術實時跟蹤土壤中的水分含量，從而準確地知道何時向作物提供水，以及如何合理節(jié)約水的消耗。這意味著農民有更多時間來做其他的重要工作，而不必費心親自灌溉作物。

據估計，地球上約70%的淡水供應用于農業(yè)生產，因此更有效地管理淡水供應將對如何利用這一寶貴資源產生連鎖反應。

3. Artificial intelligence manages irrigation and water use

For plants to grow normally, they need a constant supply of water. Growing crops is particularly difficult in areas of the world where rainwater and fresh water are scarce or unreliable. Just as your garden sprinkler can set a timer, modern AI irrigation approaches go further than that.

They can track the water content in the soil in real time through machine learning technology in agricultural environments, thus knowing exactly when to provide water to the crops and how to rationally save water consumption. This means that farmers have more time to do other important work without having to bother to irrigate the crops themselves.

It is estimated that about 70% of the planet's freshwater supply is used for agricultural production, so more efficient management of the freshwater supply will have a knock-on effect on how this valuable resource is utilized.

4、基于圖像的養(yǎng)分和肥料使用解決方案

土壤本身并不總是為作物提供最好的營養(yǎng)，農民必須定期輪作。在過去，肥料是植物的主要肥料，但農業(yè)現代化帶來了大量新的和創(chuàng)新的施肥方案。

農民花大量時間在地里以氮肥的形式為作物提供必要的營養(yǎng)，然而人工智能現在已經成為這個領域的主要參與者。

現代人工智能解決方案不僅可以檢測出需要多少肥料才能減少浪費，而且還有可用的硬件來輔助運輸過程。其中一個解決方案就是Rowbot。

這是一臺基于圖像的機器，它在作物生長期間收集植物數據，只向最需要化肥的作物提供肥料，從而提高原本收成較低的作物的產量。

由Bosch開發(fā)的Plantect是另一個智能的人工智能套件，它可以幫助農場從確定正確的陽光和濕度水平到無縫監(jiān)控一切，并與物聯網協同工作。

4. Image-based nutrient and fertilizer use solutions

The soil itself does not always provide the best nutrition for the crops, and farmers must regularly rotate them. In the past, fertilizers were the main fertilizers for plants, but agricultural modernization has brought a host of new and innovative fertilization schemes. Farmers spend a lot of time in the fields providing the necessary nutrients for their crops in the form of nitrogen fertilizer, yet AI has now become a major player in the field. Modern AI solutions can not only detect how much fertilizer is needed to reduce waste, but also have the hardware available to assist the shipping process. One of the solutions is theRowbot. It is an image-based machine that collects plant data during crop growth and provides fertilizer only to the crops that need fertilizer most, thereby increasing the yields of crops that originally have lower harvests. Developed by Bosch, Plantect is another intelligent AI suite that can help farms move from determining the right sunlight and humidity levels to seamlessly monitoring everything, and working in concert with the Internet of Things.

5、人工智能可以預測天氣狀況

從潮濕的英格蘭到太陽炙烤下的加利福尼亞，再到干旱肆虐的索馬里，天氣狀況極大地影響了農作物的生長。

一季不下雨意味著成千上萬的人在幾個月內都會挨餓。然而，人工智能現在可以與機器學習相關的特殊算法結合使用——再加上衛(wèi)星信息——以確保無論天氣如何，農作物都不會歉收。

美國一家名為aWhere的公司正在利用這種人工智能技術來預測天氣模式，使農民能夠提前采取正確的措施。

它能測量一切：從太陽輻射到降水、溫度推測和風速，以提供有關潛在作物生長和產量的準確數據。

例如，如果你知道兩天后會有大量降雨，就不需要用昂貴的灌溉用水。或者，如果你知道接下來的幾天會帶來高溫，那么你可以確保作物在早晨早些時候澆水，為溫度上升做好準備，減少土壤蒸發(fā)。

這兩者都可以被編程到AI機器解決方案中，當軟件和硬件結合在一起時，農業(yè)技術可以提前為農戶采取行動。

5. Artificial intelligence can predict weather conditions

From wet England to sun-setting California to arid Somalia, weather conditions have greatly affected crops. A season of no rain means that thousands of people will starve within a few months. However, AI can now use —— in combination with special algorithms related to machine learning plus satellite information —— to ensure that crops don't fail regardless of the weather. A US company called aWhere is using this artificial intelligence technology to predict weather patterns and allow farmers to take the right steps ahead of time. It measures everything: fromsolar radiation to precipitation, temperature speculation, and wind speed to provide accurate data on potential crop growth and yield. For example, if you know that there will be a lot of rain after two days, no expensive irrigation water is needed. Or, if you know that the next few days will bring high heat, then you can make sure that the crops are watered early in the morning in preparation for a temperature rise and reduce soil evaporation. Both can be programmed into AI machine solutions, and when software and hardware are combined, agricultural technology cantake action for farmers ahead.

6、創(chuàng)新的機器視覺來識別作物問題

一旦作物生長，就有必要保護它們的生長不受疾病和蟲害的侵蝕。在這方面，人工智能也可以提供幫助。

你不僅可以在人工智能控制機器和條件的溫室里種植作物，而且戶外作物也可以從技術投入中受益。

跨國農業(yè)企業(yè)John Deere現在收購了Blue River Technology，作為其人工智能武器庫的一部分。他們共同開發(fā)了一種“看和噴”的方法，利用人工智能機器學習和計算機視覺相結合，找出影響作物生長的雜草，然后將它們清除。

該公司發(fā)言人John May表示：“機器學習是Deere未來的一項重要能力，并且它認識到技術對我們客戶的重要性?！?/span>

“看和噴”方法意味著，他們現在可以針對特定的雜草，提高作物產量，而不是以高昂的成本噴灑整株作物，而且還會伴隨著對的健康影響。

6. Innovative machine vision to identify crop problems

Once crops are grown, it is necessary to protect their growth from disease and insect pests. In this regard, AI can also help. Not only can you grow crops in a greenhouse where AI controls machines and conditions, but outdoor crops can also benefit from technology inputs. Multinational agribusiness John Deere has now acquired Blue River Technology as part of its AI arsenal. Together, they developed a "see and spray" method, using a combination of artificial intelligence machine learning and computer vision to identify weeds that affect crops andthen remove them. Company spokesman John May said: " Machine learning is an important capability of Deere in the future, and it recognizes the importance of technology to our customers.” The see and spray approach means that they can now target specific weeds and increase crop yields, rather than spraying whole crops at a high cost, along with their health effects.

7、用人工智能技術監(jiān)測雜草和害蟲問題

人工智能傳感器也正在開發(fā)中，利用圖像傳感技術來檢測植物葉片的病害特征。這與通過人工智能機器進行的彩色成像有關。人工智能機器能夠區(qū)分健康和患病的葉子，然后通過與機器人集成來去除它們。

微軟開發(fā)人員也在使用同樣的技術，他們合作開發(fā)了一個害蟲預測界面，可以識別破壞農作物的昆蟲。在很短的時間內，這將包括診斷和消滅害蟲的實際遠程機器視覺。

這項技術最多可以減少80%的化學物質的使用，而花在除草劑上的錢會減少90%。

雜草控制對農民來說非常重要，因為目前約有250個品種對現代除草劑具有抗藥性，僅大豆和玉米作物上的雜草生長每年就造成400多億美元的損失。

7. Using artificial intelligence technology to monitor weeds and pests

AI sensors are also being developed to use image-sensing techniques to detect disease characteristics in plant leaves. This is related to color imaging via an AI machine. AI machines are able to distinguish healthy from diseased leaves and then remove them by integrating with the robot. Microsoft developers, who have collaborated to develop a pest prediction interface that identifies insects that damage crops. In a short time this will include diagnosis and elimination of pests by actual remote machine vision. The technology could reduce the use of chemicals by up to80%, and reduce the money spent on herbicides by 90%. Weed control is very important for farmers because about 250 varieties are currently resistant to modern herbicides, and weeds growing on soybean and corn crops alone cost more than $40 billion a year.

8、預測正確的收獲時間

幾個世紀以來，農民們一直在考慮天氣狀況和作物的總體狀況等因素，決定最佳收割時間

由于成像技術反饋給遠程學習軟件，人工智能現在帶來了一個決定作物是否可以采摘的新元素。

該技術可以用白色和UVA型燈分析水果的成熟度，這意味著農民可以選擇只采摘最成熟的水果或蔬菜，而把其他未成熟的水果留一段時間。

這可以在溫室里小規(guī)模地進行，也可以在更大的規(guī)模上進行，使用直升機和無人機可以構建一個整體的田間管理地圖。

8. Predict the right harvest time

For centuries, farmers have considered the weather conditions and the overall state of their crops to determine the best harvest time With imaging feeds back to remote learning software, AI now brings a new element to determine whether crops can be picked. The technique can analyze the maturity of fruit with white and UVA lamps, meaning that farmers can choose to pick only the most mature fruits or vegetables while leaving the other immature fruits for a period of time. This can be done on a small scale in a greenhouse or on a larger scale, using helicopters anddrones to build a holistic map of field management.

9、機械收割方法

現在讓我們看看食物是如何挑選的。越來越多的農場工人不愿意日復一日地做重復性的、季節(jié)性的采摘水果和蔬菜的工作，預計在2014年至2024年間，這一比例將降至6%。

我們面臨著這樣的事實上：由于工人短缺，熟透的水果往往無法采摘，這意味著利潤的損失。

根據農業(yè)綜合企業(yè)的性質，一個農場大約40%的利潤用于體力勞動和工資。

人工智能可以大幅減少這一數字，因為一旦購買了機器，它們就會隨著時間的推移為自己買單。

有兩個機器收割的例子來自Harvest CROO Robotics，它創(chuàng)造了采摘成熟草莓的硬件，以及擁有可以收割蘋果園的機器的豐富技術。這種類型的人工智能將感知和動作結合在一起，因此自主機器可以看到需要收獲什么，然后繼續(xù)執(zhí)行收獲的動作。

9. Mechanical harvesting methods

Now let's see how the food is chosen. The growing reluctance of farm workers to do repetitive, seasonal work of fruit and vegetables is expected to fall to 6% between 2014 and 2024. We face the fact that because of a shortage of workers, ripe fruit is often not picked, which means a loss of profits. Depending on the nature of the agribusiness, about 40% of the profits of a farm go to manual labor and wages. AI can dramatically reduce that number, because once they buy machines, they pay for themselves over time. There are two examples of machine harvesting from Harvest CROO Robotics, which createsthe hardware for picking ripe strawberries, and the rich technology of having machines that can harvest apple orchards. This type of AI combines perception and action, so that the autonomous machine can see what needs to be harvested, and then continue to perform the harvested action.

10、農場機器接受人工智能升級

現代農業(yè)往往使用各種各樣的機器來保持生產效率。

從拖拉機和收割機到四軸腳踏車和運貨卡車，機器是農業(yè)的重要組成部分，但是機器故障和持續(xù)的維護是一個嚴重但經常被忽視的影響利潤的問題。像汽車這樣的普通道路交通工具，現在正在用一組非同尋常的電子產品進行制造，從輪胎壓力到油位，這些電子產品可以提供各種反饋。

未來的農業(yè)機械也將采用同樣先進的監(jiān)測系統。與其等著拖拉機在田里拋錨，還不如提前警告農民任何故障。與物聯網相結合，這些物品甚至可以在問題出現之前就預先提醒和維修。

10. Farm machines accept artificial intelligence upgrade

Modern agriculture often uses a wide variety of machines to maintain production efficiency. From tractors and harvesters to quad bicycles and cargo trucks, machines are an important part of agriculture, but machine failure and continuous maintenance is a serious but often overlooked problem affecting profits. Ordinary road vehicles like cars are now being manufactured with an unusual set of electronics that can provide a variety of feedback, from tire pressure to oil levels. Future agricultural machinery will also use the same advanced monitoring system. Instead of waiting for the tractor to break down in the field, just warn the farmers of any problems. Combined with the Internet of Things, these items can be alerted and repaired even before problems arise.

11、人工智能無人機的崛起

展望未來，無人機已經在許多方面得到了應用，要使現有的無人機適應農業(yè)生產，所需要的只是硬件和軟件的集成，這為這些飛行器提供了額外的用途。

像VineView所使用的智能攝像頭，可以在很遠的地方為農民提供反饋和信息——從作物生長受阻和缺水到土壤條件和病蟲害監(jiān)測。未來的農民不再需要步行數英里穿過他們的莊稼和農田來評估它的狀況——而是用無人機在幾分鐘內飛去所關注的地區(qū)。

到2027年，農業(yè)無人機的市場份額預計將接近5億。無人駕駛拖拉機也將成為現實，在沒有真人指導的情況下，通過編程使其以一定的速度行駛，同時以有效的方式執(zhí)行特定任務。

11. The rise of AI drones

Looking ahead, UAVs have been used in many ways, and all is needed to adapt existing UAVs to agricultural production is the integration of hardware and software, which provides additional uses for these vehicles. Smart cameras like those used by VineView can provide farmers with feedback and information —— from crop growth block and water shortage to soil conditions and pest monitoring. Future farmers will no longer need to walk miles through their crops and farmland to assess its condition —— but will use drones to fly to the areas of interest in minutes. By 2027, the market share of agricultural drones is expected to approach $500 million. Driverless tractors will also become a reality, being programmed to drive at a certain speed while performing specific tasks in an effective way。

12、來自數據庫的云共享信息可以幫助農民

由于“Alexa”類型的系統為農民的所有問題提供了解決方案，人工智能可以成為農民最好的朋友。

建立農業(yè)的知識數據庫，并能向其詢問從動物疾病到土壤質量的一切問題。這樣的基礎可以學習正確的解決方案和回答問題，然后可以有效地與業(yè)務中的其他人共享。

當農業(yè)在很大程度上實現自動化時，數據共享無疑將具有重要性。訓練系統需要數據，特別是人工智能算法的數據非常有價值。

近年來，農業(yè)數據聯盟(Agricultural Data Coalition)已成立，旨在幫助農民掌握信息和數據處理技術，以便從研究人員到農場主、農作物買家和保險公司等所有人都能共同努力，提高產量，從而提高所有人的利潤。

得益于人工智能技術，總體產量得以提高，將人工智能應用于農業(yè)的最終目標是提高每平方英尺的作物產量。

產量的提高主要是通過模仿人類認知的算法實現的，在分析大數據時，將農業(yè)中的機器學習技術帶到最前沿，并利用它做出有效的決策。這些數學人工智能公式可以通過決定作物從播種到收獲的最佳操作過程來幫助提高作物產量。

人工智能解決方案在農業(yè)領域的技術有很多，而且具有幾乎無限的潛力。農業(yè)傳感器可以看到外形，識別語音命令和操作視覺感知能力來收集所需的數據。

信息管理系統控制收集的數據，并允許人工智能軟件基于深度學習技術和機器學習通過預測分析做出決策。這些數據可以用于專門為農業(yè)綜合企業(yè)制造的硬件，比如自動無人機和自動駕駛汽車。

充分利用收集到的數據，能為農民提供最好的服務。農業(yè)領域的人工智能解決方案要想在這一領域起飛，就需要在農業(yè)實踐中集成人工智能的多方優(yōu)勢。

12. Cloud-sharing information from databases can help farmers

Because the "Alexa" -type systems provide solutions to all the farmers 'problems, AI can be the farmer's best friend. Establish a knowledge database of agriculture and ask them about everything from animal disease to soil quality. Such a foundation can learn the correct solutions and answer questions, which can then be effectively shared with others in the business. When agriculture is largely automated, data sharing will undoubtedly be important. Training systems require data, especially data for AI algorithms, which is very valuable. In recent years, the Agricultural Data Alliance (Agricultural Data Coalition)has been established to help farmers master information and data processing technology so that everyone from researchers to farmers, crop buyers and insurance companies can work together to increase production and thus increase profits for all. Thanks to AI technology, overall production has increased, and the ultimate goal of applying AI to agriculture is to increase crop production per square foot.

potential. Agricultural sensors can see the shape, recognize voice commands and manipulate the visual perception ability to collect the required data. The information management system controls the collected data and allows AI software to make decisions through predictive analysis based on deep learning techniques and machine learning. The data can be used for hardware made specifically for agribusinesses, such as autonomous drones and self-driving vehicles. Make full use of the collected data to provide the best service for farmers. For AI solutions in agriculture to take off in thisarea, it requires integrating multiple advantages of AI in agricultural practice.

13、“農業(yè) 4.0 ”指即將來臨的智能農業(yè)

我國農業(yè)科學家瞄準“農業(yè)4.0”，起步晚，但進神速，是一種“彎道超車”模式?；ヂ摼W時代農業(yè)通過網絡、信息等進行資源軟整合， 在大數據、云計算、互聯網、傳感器的基礎之上形成智能農業(yè)。 “農業(yè) 4.0 ”是利用農業(yè)標準化體系的系統方法對農業(yè)生產進行統一管理，所有過程均是可控、高效的。 農業(yè)服務者與農業(yè)生產者之間的信息通道通過農業(yè)標準化平臺實現對等連接， 使整個過程中的互動性更強。

13. Agriculture 4.0, which refers to the upcoming smart agriculture

Chinese agricultural scientists aim at "agriculture 4.0", started late, but into the speed, is a "curve overtaking" mode. In the Internet era, agriculture soft integrates resources through network and information, forming intelligent agriculture on the basis of big data, cloud computing, Internet and sensors."Agriculture 4.0" is the system method of agricultural standardization system to conduct unified management of agricultural production, and all the processes are controllable and efficient. The information channel between agricultural service providers and agricultural producers is peer-to-peer connectedthrough the agricultural standardization platform, making the interaction in the whole process stronger.

14、發(fā)展智慧農業(yè)的社會需求

伴隨著我國工業(yè)化和城市化發(fā)展， 農業(yè)人口出現下降是一個必然的趨勢。對于其他產業(yè)而言，從事農業(yè)勞動的收益相對較少，年輕人普遍不愿意繼承，導致農業(yè)生產呈現出“ 后繼無人”的窘境。
如果不改變農業(yè)生產的傳統形象，那么很難吸引年輕勞動力向農業(yè)部門轉移。
提高農業(yè)競爭力，就需要順應現代科技發(fā)展潮流，把大數據、機器人和人工智能等先進技術引入農業(yè)生產過程，改造傳統的農業(yè)發(fā)展形態(tài)，實現從經驗種田到智慧種田的轉變。推動發(fā)展智慧農業(yè)，推動農業(yè)向信息化、智能化方向發(fā)展。
農業(yè)物聯網的推廣不僅可以大幅減輕農業(yè)勞作的壓力（農戶應用信息技術來解決農業(yè)生產中的播種、控制、質量安全以及成本削減等問題），提升農業(yè)對青年人和女性勞動者的吸引力，解決農業(yè)生產勞動力短缺的問題，而且大大提升了農業(yè)的生產能力和效率，有助于促進各地生產出高附加值和高品質的農產品，獲得了很好的經濟效益和生態(tài)效益，增強農業(yè)的魅力和國際競爭力。

14. The social need of developing smart agriculture

With the development of China's industrialization and urbanization, the decline of agricultural population is an inevitable trend. For other industries, the benefits of agricultural labor are relatively small, and young people are generally unwilling to inherit it, leading to the dilemma of "no successor" in agricultural production. Without changing the traditional image of agricultural production, it will be difficult to attract younger workers to the agricultural sector. To improve agricultural competitiveness, it is necessary to follow the trend of modern science andtechnology development, introduce advanced technologies such as big data, robots and artificial intelligence into the agricultural production process, transform the traditional form of agricultural development, and realize the transformation from experienced farming to intelligent farming. We will promote the development of smart agriculture and promote the development of agriculture toward information application and intelligence.

agricultural work (farmers apply information technology to solve the planting, control, quality safety and cost reduction), improve the agricultural attractive for young people and female workers, solve the problem of agricultural labor shortage, and greatly improve the agricultural production capacity and efficiency, help to promote around produce high value-added and high quality agricultural products, obtained a good economic and ecological benefits, enhance the charm of agriculture and international competitiveness.