现代农业智能改造传统农业的14种方式【中英双语】

随着我们进入机器学习的新技术时代，人工智能和农业正变得如胶似漆。它带来了令人兴奋的无限可能性：从种子发芽，到保持作物的完整性，再到实际的收获过程。科学家估计到2050年，全球人口将增加到97亿人以上，那时很多饥饿的人口需要养活。相比于人口的大量增长，耕地面积只会增加4%。因此，解决办法不是扩大农田来种植庄稼和饲养牲畜，而是更有效地利用现有的土地。回顾过去，我们看到大约70年前“绿色革命”的开始，它带来了灌溉系统的改善，农田机械化的方法，以及新型的人造肥料。这些因素的叠加起来提高了粮食产量，全球约有10亿人因此从饥饿中获救。这种快速发展带来了许多好处，如更高的产量，但也有许多负面因素：种植业大量使用杀虫剂、化肥等激素，破坏了生物多样性，一些不可或缺的生物灭绝。同时，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，也耗尽了土壤的自然肥力。

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.

可持续农业和粮食问题专家Danielle Nierenberg说：“这些方法从来没打算长期使用。”如果我们要继续保持粮食生产的稳定和充足，就必须进行变革。目前，全球20%的人口受雇于农业综合企业，这是一个价值3万亿美元的产业。但是我们如何进行这个变换呢？答案可以在人工智能和农业的融合发展中找到。新型的机器学习技术在农业各个解决方案中是如何推动生产的？人工智的大量应用，改善了发展中国家和已经领先的西方国家的农业状况：

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、人工智能选种

如果我们想要有最好的作物，那么这一切都取决于我们种植的种子的基因。Monsanto公司现在正在使用人工智能扫描具有最理想特性的种子的DNA序列。农民将不再需要投入时间和精力来进行种子的交叉变异实验，因为现在有计算机程序可以为他们进行这种分析。种子本身有发芽率，或“种子休眠”，这意味着它们只有在特定条件下才会发芽和开始生长。研究人员可以利用人工智能找出种子发芽的最佳条件，如温度和湿度水平，使作物能够比预期的更早开始生长。这减少了等待时间，并可以使作物全年种植。机器学习支持的图像分析的新应用，加上移动成像的自动化控制，可以测试种子的表型，以确定使用哪种种子最好。这方面的实例可以在种子发芽技术中找到，该技术已经用于测试番茄和玉米等作物。

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、通过人工智能反馈进行土壤管理

在世界各地种植农作物时，土壤营养也会发挥作用。通过特殊的算法，深度学习被带到这里的最前沿，这些算法可以帮助监测种植前和生长过程中土壤的健康状况。

土壤退化和侵蚀也是影响农作物生长的重要因素，但这两个问题都可以用人工智能解决，就像PEAT公司在德国做过的实验那样。他们开发了一种能分析土壤缺陷的Plantix。加上无人机的视觉感知能力，它们可以探测到作物的生长区域，这些作物可能生长在有缺陷的土壤中，或会遭受区域里疾病和害虫的侵袭。

它通过对叶子成像，然后通过一个软件运行，这个软件可以区分正常和不健康的生长模式。更重要的是，软件会向农民提出解决问题的方法。

CropDiagnosis是另一个类似的应用程序，它可以用无人机扫描整个领域，并且评估土壤中灌溉和氮含量水平。

在美国，Trace Genomics也在追随他们的脚步，采用基于人工智能的技术来研究土壤弱点和作物缺陷。

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

植物要想正常生长，就需要持续不断的供给需要的水。在世界上雨水和淡水稀少或不可靠的地区，种植作物尤其困难。就像你的花园洒水器可以设置定时器一样，现代的人工智能灌溉方法比这更进一步。

他们可以通过农业环境中的机器学习技术实时跟踪土壤中的水分含量，从而准确地知道何时向作物提供水，以及如何合理节约水的消耗。这意味着农民有更多时间来做其他的重要工作，而不必费心亲自灌溉作物。

据估计，地球上约70%的淡水供应用于农业生产，因此更有效地管理淡水供应将对如何利用这一宝贵资源产生连锁反应。

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、基于图像的养分和肥料使用解决方案

土壤本身并不总是为作物提供最好的营养，农民必须定期轮作。在过去，肥料是植物的主要肥料，但农业现代化带来了大量新的和创新的施肥方案。

农民花大量时间在地里以氮肥的形式为作物提供必要的营养，然而人工智能现在已经成为这个领域的主要参与者。

现代人工智能解决方案不仅可以检测出需要多少肥料才能减少浪费，而且还有可用的硬件来辅助运输过程。其中一个解决方案就是Rowbot。

这是一台基于图像的机器，它在作物生长期间收集植物数据，只向最需要化肥的作物提供肥料，从而提高原本收成较低的作物的产量。

由Bosch开发的Plantect是另一个智能的人工智能套件，它可以帮助农场从确定正确的阳光和湿度水平到无缝监控一切，并与物联网协同工作。

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、人工智能可以预测天气状况

从潮湿的英格兰到太阳炙烤下的加利福尼亚，再到干旱肆虐的索马里，天气状况极大地影响了农作物的生长。

一季不下雨意味着成千上万的人在几个月内都会挨饿。然而，人工智能现在可以与机器学习相关的特殊算法结合使用——再加上卫星信息——以确保无论天气如何，农作物都不会歉收。

美国一家名为aWhere的公司正在利用这种人工智能技术来预测天气模式，使农民能够提前采取正确的措施。

它能测量一切：从太阳辐射到降水、温度推测和风速，以提供有关潜在作物生长和产量的准确数据。

例如，如果你知道两天后会有大量降雨，就不需要用昂贵的灌溉用水。或者，如果你知道接下来的几天会带来高温，那么你可以确保作物在早晨早些时候浇水，为温度上升做好准备，减少土壤蒸发。

这两者都可以被编程到AI机器解决方案中，当软件和硬件结合在一起时，农业技术可以提前为农户采取行动。

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、创新的机器视觉来识别作物问题

一旦作物生长，就有必要保护它们的生长不受疾病和虫害的侵蚀。在这方面，人工智能也可以提供帮助。

你不仅可以在人工智能控制机器和条件的温室里种植作物，而且户外作物也可以从技术投入中受益。

跨国农业企业John Deere现在收购了Blue River Technology，作为其人工智能武器库的一部分。他们共同开发了一种“看和喷”的方法，利用人工智能机器学习和计算机视觉相结合，找出影响作物生长的杂草，然后将它们清除。

该公司发言人John May表示：“机器学习是Deere未来的一项重要能力，并且它认识到技术对我们客户的重要性。”

“看和喷”方法意味着，他们现在可以针对特定的杂草，提高作物产量，而不是以高昂的成本喷洒整株作物，而且还会伴随着对的健康影响。

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、用人工智能技术监测杂草和害虫问题

人工智能传感器也正在开发中，利用图像传感技术来检测植物叶片的病害特征。这与通过人工智能机器进行的彩色成像有关。人工智能机器能够区分健康和患病的叶子，然后通过与机器人集成来去除它们。

微软开发人员也在使用同样的技术，他们合作开发了一个害虫预测界面，可以识别破坏农作物的昆虫。在很短的时间内，这将包括诊断和消灭害虫的实际远程机器视觉。

这项技术最多可以减少80%的化学物质的使用，而花在除草剂上的钱会减少90%。

杂草控制对农民来说非常重要，因为目前约有250个品种对现代除草剂具有抗药性，仅大豆和玉米作物上的杂草生长每年就造成400多亿美元的损失。

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、预测正确的收获时间

几个世纪以来，农民们一直在考虑天气状况和作物的总体状况等因素，决定最佳收割时间

由于成像技术反馈给远程学习软件，人工智能现在带来了一个决定作物是否可以采摘的新元素。

该技术可以用白色和UVA型灯分析水果的成熟度，这意味着农民可以选择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时间。

这可以在温室里小规模地进行，也可以在更大的规模上进行，使用直升机和无人机可以构建一个整体的田间管理地图。

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、机械收割方法

现在让我们看看食物是如何挑选的。越来越多的农场工人不愿意日复一日地做重复性的、季节性的采摘水果和蔬菜的工作，预计在2014年至2024年间，这一比例将降至6%。

我们面临着这样的事实上：由于工人短缺，熟透的水果往往无法采摘，这意味着利润的损失。

根据农业综合企业的性质，一个农场大约40%的利润用于体力劳动和工资。

人工智能可以大幅减少这一数字，因为一旦购买了机器，它们就会随着时间的推移为自己买单。

有两个机器收割的例子来自Harvest CROO Robotics，它创造了采摘成熟草莓的硬件，以及拥有可以收割苹果园的机器的丰富技术。这种类型的人工智能将感知和动作结合在一起，因此自主机器可以看到需要收获什么，然后继续执行收获的动作。

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、农场机器接受人工智能升级

现代农业往往使用各种各样的机器来保持生产效率。

从拖拉机和收割机到四轴脚踏车和运货卡车，机器是农业的重要组成部分，但是机器故障和持续的维护是一个严重但经常被忽视的影响利润的问题。像汽车这样的普通道路交通工具，现在正在用一组非同寻常的电子产品进行制造，从轮胎压力到油位，这些电子产品可以提供各种反馈。

未来的农业机械也将采用同样先进的监测系统。与其等着拖拉机在田里抛锚，还不如提前警告农民任何故障。与物联网相结合，这些物品甚至可以在问题出现之前就预先提醒和维修。

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、人工智能无人机的崛起

展望未来，无人机已经在许多方面得到了应用，要使现有的无人机适应农业生产，所需要的只是硬件和软件的集成，这为这些飞行器提供了额外的用途。

像VineView所使用的智能摄像头，可以在很远的地方为农民提供反馈和信息——从作物生长受阻和缺水到土壤条件和病虫害监测。未来的农民不再需要步行数英里穿过他们的庄稼和农田来评估它的状况——而是用无人机在几分钟内飞去所关注的地区。

到2027年，农业无人机的市场份额预计将接近5亿。无人驾驶拖拉机也将成为现实，在没有真人指导的情况下，通过编程使其以一定的速度行驶，同时以有效的方式执行特定任务。

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、来自数据库的云共享信息可以帮助农民

由于“Alexa”类型的系统为农民的所有问题提供了解决方案，人工智能可以成为农民最好的朋友。

建立农业的知识数据库，并能向其询问从动物疾病到土壤质量的一切问题。这样的基础可以学习正确的解决方案和回答问题，然后可以有效地与业务中的其他人共享。

当农业在很大程度上实现自动化时，数据共享无疑将具有重要性。训练系统需要数据，特别是人工智能算法的数据非常有价值。

近年来，农业数据联盟(Agricultural Data Coalition)已成立，旨在帮助农民掌握信息和数据处理技术，以便从研究人员到农场主、农作物买家和保险公司等所有人都能共同努力，提高产量，从而提高所有人的利润。

得益于人工智能技术，总体产量得以提高，将人工智能应用于农业的最终目标是提高每平方英尺的作物产量。

产量的提高主要是通过模仿人类认知的算法实现的，在分析大数据时，将农业中的机器学习技术带到最前沿，并利用它做出有效的决策。这些数学人工智能公式可以通过决定作物从播种到收获的最佳操作过程来帮助提高作物产量。

人工智能解决方案在农业领域的技术有很多，而且具有几乎无限的潜力。农业传感器可以看到外形，识别语音命令和操作视觉感知能力来收集所需的数据。

信息管理系统控制收集的数据，并允许人工智能软件基于深度学习技术和机器学习通过预测分析做出决策。这些数据可以用于专门为农业综合企业制造的硬件，比如自动无人机和自动驾驶汽车。

充分利用收集到的数据，能为农民提供最好的服务。农业领域的人工智能解决方案要想在这一领域起飞，就需要在农业实践中集成人工智能的多方优势。

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、“农业 4.0 ”指即将来临的智能农业

我国农业科学家瞄准“农业4.0”，起步晚，但进神速，是一种“弯道超车”模式。互联网时代农业通过网络、信息等进行资源软整合， 在大数据、云计算、互联网、传感器的基础之上形成智能农业。 “农业 4.0 ”是利用农业标准化体系的系统方法对农业生产进行统一管理，所有过程均是可控、高效的。 农业服务者与农业生产者之间的信息通道通过农业标准化平台实现对等连接， 使整个过程中的互动性更强。

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、发展智慧农业的社会需求

伴随着我国工业化和城市化发展， 农业人口出现下降是一个必然的趋势。对于其他产业而言，从事农业劳动的收益相对较少，年轻人普遍不愿意继承，导致农业生产呈现出“ 后继无人”的窘境。
如果不改变农业生产的传统形象，那么很难吸引年轻劳动力向农业部门转移。
提高农业竞争力，就需要顺应现代科技发展潮流，把大数据、机器人和人工智能等先进技术引入农业生产过程，改造传统的农业发展形态，实现从经验种田到智慧种田的转变。推动发展智慧农业，推动农业向信息化、智能化方向发展。
农业物联网的推广不仅可以大幅减轻农业劳作的压力（农户应用信息技术来解决农业生产中的播种、控制、质量安全以及成本削减等问题），提升农业对青年人和女性劳动者的吸引力，解决农业生产劳动力短缺的问题，而且大大提升了农业的生产能力和效率，有助于促进各地生产出高附加值和高品质的农产品，获得了很好的经济效益和生态效益，增强农业的魅力和国际竞争力。

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.