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[前沿资讯] 手机拍一拍番茄“医生”来 进入全文

农业科技报

也许,你只需要拿出手机,打开摄像头,对着田地里的番茄拍张照,就能避免一场导致“颗粒无收”的疫病。近日,在线发表于《自然—植物》的一篇论文描述了一款可以检测微生物侵染番茄植株的智能手机传感器。该系统有望以及时发现病菌的方式,帮助对抗破坏性作物病害。目前,植物病原菌的检测主要集中在多种分子分析方法上,包括基于核酸的PCR和DNA微阵列等技术,以及免疫学方法,如基于抗体的侧流试验(LFA)和酶联免疫吸附测定(ELISA)。该研究负责人、美国北卡罗来纳州立大学的魏青山表示,基于核酸的方法是敏感和特异的,但检测方案繁琐,免疫分析技术令现场检测变得简单,但在某些应用中受到检测灵敏度和特异性的限制。于是,魏青山和同事开发了一款传感器,可以在番茄感病后的两天内检测出晚疫病。他们使用的化学修饰金纳米粒子会与病株叶片释放的挥发性有机物发生反应,而手机摄像头能够捕捉这种反应引起的颜色变化。“我们开发的这种基于智能手机的挥发性有机化合物指纹识别平台,能通过监测田间特有的叶片挥发性排放,对晚疫病进行无创诊断。”魏青山说,“该手持装置集成了一个一次性比色传感器阵列,由等离子体纳米色素和化学反应性有机染料组成,可在反应1分钟内检测关键植物挥发物水平。”“这一技术可以在肉眼看到症状前就检测出晚疫病,使人们尽早采取行动防止病害传播。如果与不同的比色法指示剂结合使用,该技术或能用于检测其他植物病害。”魏青山说。

[前沿资讯] No Need to Watch Grass Grow Anymore! 进入全文

USDA

Each spring, ranchers face the same challenge of trying to guess how much grass will be available for their livestock to graze during the summer. Ranchers make this determination relying on boots-on-the-ground observations of rangeland conditions. But now in the Northern Great Plains, ranchers have a new forecasting tool to help them with this important decision: “Grass-Cast.” With Grass-Cast, ranchers can now base their decisions on where to graze their cattle and how many cattle to release on 38 years of historical data on weather, grass growth and seasonal precipitation outlooks. Grass-Cast processes these data to predict if rangeland grasses in a rancher’s county will produce in above-normal, near-normal, or below-normal amounts. Grass-cast is first released in early May each year as three color-coded maps. The maps are then updated every two weeks. The tool improves in accuracy the deeper we go in the growing season. Ranchers also fine-tune the data with their knowledge of local plant communities, soil types, typography and other factors before making their final management decisions. The tool uses well-known relationships between historical weather and grassland production. It combines current weather data and seasonal climate outlooks with a well-trusted grassland model to predict total biomass for individual counties, compared to their 38-year average. Grass-Cast debuted in May 2018 and is a collaboration between ARS scientists in Fort Collins, Colorado, and colleagues at Colorado State University, the USDA Natural Resources Conservation Service, the National Drought Mitigation Center, and the University of Arizona.

[前沿资讯] Artificial intelligence helps banana growers protect the world's most favorite fruit 进入全文

International Center for Tropical Agriculture (CIAT)

Using artificial intelligence, scientists created an easy-to-use tool to detect banana diseases and pests. With an average 90 percent success rate in detecting a pest or a disease, the tool can help farmers avoid millions of dollars in losses. Artificial intelligence-powered tools are rapidly becoming more accessible, including for people in the more remote corners of the globe. This is good news for smallholder farmers, who can use handheld technologies to run their farms more efficiently, linking them to markets, extension workers, satellite images, and climate information. The technology is also becoming a first line of defense against crop diseases and pests that can potentially destroy their harvests. A new smartphone tool developed for banana farmers scans plants for signs of five major diseases and one common pest. In testing in Colombia, the Democratic Republic of the Congo, India, Benin, China, and Uganda, the tool provided a 90 percent successful detection rate. This work is a step towards creating a satellite-powered, globally connected network to control disease and pest outbreaks, say the researchers who developed the technology. The findings were published this week in the journal Plant Methods. "Farmers around the world struggle to defend their crops from pests and diseases," said Michael Selvaraj, the lead author, who developed the tool with colleagues from Bioversity International in Africa. "There is very little data on banana pests and diseases for low-income countries, but an AI tool such as this one offers an opportunity to improve crop surveillance, fast-track control and mitigation efforts, and help farmers to prevent production losses." Co-authors included researchers from India's Imayam Institute of Agriculture and Technology (IIAT), and Texas A&M University. Bananas are the world's most popular fruit and with the global population set to reach 10 billion in 2050, pressure is mounting to produce sufficient food. Many countries will continue depending on international trade to ensure their food security. It is estimated that by 2050 developing countries' net imports of cereals will more than double from 135 million metric tonnes in 2008/09 to 300 million in 2050. An essential staple food for many families, bananas are a crucial source of nutrition and income. However, pests and diseases -- Xanthomanas wilt of banana, Fusarium wilt, black leaf streak (or Black sigatoka), to name a few -- threaten to damage the fruit. And when a disease outbreak hits, the effects to smallholder livelihoods can be detrimental. In the few instances in which losses to the Fusarium Tropical race 4 fungus have been estimated, they amounted to US$121 million in Indonesia, US$253.3 million in Taiwan, and US$14.1 million in Malaysia (Aquino, Bandoles and Lim, 2013). In Africa, where the fungus was first reported in 2013 in a plantation in northern Mozambique, the number of symptomatic plants rose to more than 570,000 in September 2015. The tool is built into an app called Tumaini -- which means "hope" in Swahili -- and is designed to help smallholder banana growers quickly detect a disease or pest and prevent a wide outbreak from happening. The app aims to link them to extension workers to quickly stem the outbreak. It can also upload data to a global system for large-scale monitoring and control. The app's goal is to facilitate a robust and easily deployable response to support banana farmers in need of crop disease control. "The overall high accuracy rates obtained while testing the beta version of the app show that Tumaini has what it takes to become a very useful early disease and pest detection tool," said Guy Blomme, from Bioversity International. "It has great potential for eventual integration into a fully automated mobile app that integrates drone and satellite imagery to help millions of banana farmers in low-income countries have just-in-time access to information on crop diseases."

[前沿资讯] Science Simulations Support Salmon, Other Species 进入全文

USDA

How do river ecosystems support fish? How do environmental changes influence the system’s capacity to support fish? And how might different restoration strategies influence fish? These are questions J. Ryan Bellmore, a research fish biologist who works in Juneau, Alaska, for the USDA Forest Service’s Pacific Northwest Research Station, and his partners set out to answer. River restoration is typically aimed at recovering or conserving one or two target species like salmon or trout. It also influences all the other river species and the larger food web—the natural interconnection of many food chains made up of animals and plants that connect in many ways. “To successfully conserve and restore one species, we need to know how the larger food web responds to our efforts,” said Bellmore. Bellmore and his partners recently published a report describing a model that can help address specific river research and management questions. This model—the Aquatic Trophic Productivity (ATP) computer simulation—is an interactive tool that links the success of fish populations to the food webs and the conditions that influence them. The model separates aquatic organisms into “trophic groups” that share similar predators and prey. These relationships are then linked to the physical and chemical conditions of the river, including the movement of the water and the structure and composition of vegetation along the river’s edge. Recently, the ATP model was used to explore food web and fish responses to dam removal and floodplain restoration on rivers in Washington state. The model is currently being used to explore how climate change will impact the capacity for Alaskan rivers to continue to support abundant salmon populations in the future.

[前沿资讯] 陕西长武:智慧农业让农民在手机上“种苹果” 进入全文

农业科技报

“以前判断果园灌溉全凭经验,不知道果树到底缺不缺水、缺多少水。有了这套现代化设备,就可以根据检测数据科学浇水,光浇水这一项,一年就能节省5千多元。”7月24日,农业科技报陕西千亿级苹果产业全媒体采访团来到陕西省咸阳市长武县亭口镇三丰农业有限公司,记者在公司的苹果种植基地见到了负责人文娟,提到果园科学管理,她深有感触。 “在这里看果园不一定要去果园,只需鼠标轻轻一点,通过一台大型立体电子显示屏,就能直观地展示园区不同方位、不同检测项目的画面,还能进入系统管理后台,看到自动生成的各种气象、土壤、水肥数据。园区全貌清晰可见,像临近村庄处、道路拐角处等边角部位也都在可看范围之内。画面放大后,每一片树叶上的纹路真实可辨。无论身在何处,足不出户,只要一个手机,园区情况一目了然。”文娟这样说道。 文娟告诉记者,经过几年的努力,在政府的帮扶下,截至目前,园区除了安装40多个高清摄像头,还建有包括气象站在内的各类监测传感设备,实时监测基地的土壤湿度、温度、风速、雨量等信息,同时传输到手机客户端和电脑软件上。只要下载安装“智慧农业”电脑软件或手机APP,坐在家里就能随时了解果园情况,随时查阅生成的各类果园数据,更精准、更科学管理果园。 记者在采访中了解到,三丰农业启动实施“智慧农业”项目以来,在种植苹果时通过网络信号传输,运用互联网平台,利用软件系统后台收集农作物生长各项数据,视频监控实时查看生长现状,真正为苹果整个生长周期提供环境监测、生产技术、质量追溯、市场信息、品牌推广等全方位服务,实现苹果优质高产,更好地提升农产品质量。 此外,三丰农业通过“智慧农业”智能化管理,还实现了农产品质量追溯,让客商了解苹果生产和管理的全过程,实现农产品质量安全的有效监管,为进军高端水果市场打牢基础,真正享受到科技带来的实惠。 “未来我想把智慧农业做的更细更精,把好的经验做法和管理模式推广出去,有效利用苹果产业中的高端、前沿管理技术,将设施设备水平提升一个层次,带领更多的公司实现果品更优质,效益最大化。”文娟向记者谈起了将来的打算滔滔不绝。目前,这套“高大上”的设备已运行近2年了,尝到甜头的文娟对“智慧农业”的前景非常看好,她还主动申请将园区作为智慧农业试验田。

[前沿资讯] 江西启动农产品追溯平台互联互通工作 进入全文

农民日报

为加快实现全国追溯平台业务协同和信息共享,全面提升智慧监管能力和大数据分析决策水平,为追溯企业和消费者提供更加便捷化的服务,江西省委、省政府高度重视,迅速启动了省级追溯平台与国家追溯平台对接工作。省农业农村厅成立了以分管厅领导为组长的对接工作领导小组,健全市、县两级追溯体系联络员队伍,出台技术对接方案,稳步推进对接工作顺利实施。截至目前,江西省农产品质量安全监管机构、检测机构、执法机构的省、市、县三级账号已开通并发放完毕。116个监管机构(含1个省级、11个市级和104个县级)已完成了注册工作,15个检测机构(含1个省级、2个市级和12个县级)及13个执法机构(含1个省级、12个县级)完成注册工作。

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