Skip to main content

Mike Gore, Ph.D. '09, plant geneticist in the College of Agriculture and Life Sciences, explains corn breeding at Musgrave Research Farm in Aurora, New York, in August.


Mike Gore, Ph.D. ’09, hears the clock ticking. And while it’s not an alarm clock, it’s part of what gets him going every day.

Gore, associate professor of molecular breeding and genetics for nutritional quality, Liberty Hyde Bailey professor and international professor of plant breeding and genetics, conducts research at the intersection of several disciplines. His lab uses quantitative genetics, genomics, analytical chemistry and remote sensing to explore the genetic basis of trait variation in crops such as corn, oat and cassava.

A TerraSentia robot, which is being trained to perform remote diagnostics on individual corn plants, moves between rows of corn at Musgrave Research Farm in Aurora, New York.

Plant breeding has been going on for 10,000 years, he said, but technology – unmanned aerial vehicles (UAVs), robots, artificial intelligence (AI) and machine learning – is revolutionizing the practice.

“One role that we plant breeders can play is to learn how to integrate these cutting-edge technologies into research programs,” he said, “so that we can more efficiently and effectively select [plant variations] for the high-yielding, or highly nutritious, cultivar that can help feed the world’s population.”


Feeding the world’s population: It’s a huge challenge for plant breeders, he said, as well as researchers in other disciplines. Cornell is addressing it with the Cornell Initiative for Digital Agriculture (CIDA), which is leveraging digital innovations in agriculture to improve the sustainability, profitability, resiliency and efficiency of the world’s food systems. Gore is in the CIDA leadership group.

Currently at around 7.6 billion people, Earth’s population is expected to reach around 10 billion by 2050. How will we feed all those people in an efficient and sustainable way?

Gore admits that, although there’s still time to come up with viable solutions, he’s feeling the urgency.

“I think all plant scientists do,” Gore said. “We all share that passion, but we definitely hear that clock ticking. Let’s hope it’s not a timebomb.”

Gore’s lab uses “rapid phenotyping” – the ability to non-destructively measure a plant’s morphological, physiological, and biochemical properties in real time repeatedly over the course of a growing season, as opposed to waiting until harvest. That could help reduce the time it takes to develop crop varieties that are optimal for a particular region or climate.

“With these new technologies, we’re able to do phenotyping every day, every week, every month, to know how the plant is responding to the environment over whole growing seasons,” Gore said.

Among other crops, Gore’s lab focuses on corn – including corn grown in upstate New York – and the development of variations that are best suited to the short growing season and weather conditions. His lab employs camera-wielding UAVs – drones – and four-wheeled robots to perform real-time diagnostics of scores of corn varieties at the Musgrave Research Farm in Aurora, New York, about 24 miles north of campus.

This past summer, his shared 3-acre cornfield contained approximately 800 highly diverse hybrids, each in two-row mini-plots, from which his team will try to identify the best varieties for growing in the upstate region.

Gore’s team – in collaboration with the lab of Ed Buckler, adjunct professor of plant breeding and genetics – is developing AI for the autonomous vehicles that can count individual plants, measure plant height and check individual leaves for disease, among other tasks. And he can perform diagnostics on the plant at any point in its growth process.

“It’s like knowing a baseball player’s batting average in July, as opposed to just at the end of the season,” he said. “We’re trying to identify the key plant developmental stage that you can do the phenotyping on, so that it could be predictive of yield at the end of the season.

“If you had that capability,” he said, “then you’d know what plants to cross-breed before the pollen’s even been shed.”

By using technology to detect key traits in midseason, Gore said, he can perhaps develop more precise breeding methods – and shorten the breeding timeline “from six to eight years, to maybe four or five” as the technologies are developed.

He envisions a day when a robot or drone can not only facilitate rapid phenotyping, but also detect fungal diseases or weeds and immediately dispense a fungicide or herbicide in a precise dose, at just the right coordinate in the field. And while there will always be humans on a farm, Gore thinks a role-reversal could be in the offing.

“If we can train the robots, perhaps someday the robots will be training us to do very precise plant breeding,” he said. “We have more than 800 highly diverse hybrids in this field [at Musgrave]. Which one is the best for growing here, and why? Those are the questions we’re trying to answer. … We’re trying to closely model the biological reality of a plant. I would argue that, over time, robots can probably do it even better than human beings. That’s what we’re kind of on the cusp of right now.”

Developing a corn variety that’s best suited for upstate New York is one of many challenges Gore and researchers like him are tackling as the specter of feeding 10 billion people looms.

“All of these tools are going to be important for food and nutrition security,” he said. “How do we figure out how to use these technologies for crops such as cassava, rice, maize, wheat that all of these developing nations are relying on for nourishment?

“How do we turn the engine of evolution faster in plant breeding?” he asked. “We have to totally change the paradigm that we’ve been in for the past 10,000 years.”




花姿直播下载app 蝴蝶直播app下载 恋夜秀场下载app 橘子视频app下载 心上人直播app下载 火爆社区下载app视频免费最新 咪咪直播下载app 烟花巷直播app下载 花心视频app下载 春水堂视频app下载 Avnight下载app 年轻人片下载app视频免费最新 烟花巷app下载 向日葵视频app下载 花心社区下载app avgoapp下载 橙子视频app下载 樱花视频app下载 趣播app下载 豆奶视频下载app视频免费最新 皮卡丘直播app下载 米老鼠直播app下载 茄子下载app 盘她下载app 野花视频下载app 番茄直播下载app 9uu下载app 鸭脖视频下载app 雨燕直播app下载 樱花视频app下载 四虎app下载 lutube下载app 樱花雨直播下载app 十里桃花直播下载app 69热app下载 污软件下载app视频免费最新 丝瓜下载app 月光宝盒直播下载app 花狐狸直播下载app 花狐狸直播app下载 葫芦娃视频下载app 红杏视频下载app 千层浪视频下载app 名优馆app下载 笔芯直播app下载 抖阴视频下载app 草榴直播app下载 番茄直播app下载 荔枝app下载 内裤直播下载app 年华直播app下载 s8视频app下载 香蕉app下载 麻豆传媒视频app下载 鸭脖视频下载app 蓝精灵直播app下载 樱花直播下载app 棉花糖直播app下载 杏吧直播下载app 柠檬直播下载app 浪浪视频app下载 夜猫视频app下载 avgoapp下载 葫芦娃视频app下载 盘他直播app下载 花姿app下载 avgoapp下载 野花视频下载app 荔枝下载app 本色视频下载app 玉米视频下载app视频免费最新 大小姐直播下载app 最污直播app下载 朵朵直播下载app 午夜直播app下载 向日葵下载app 月亮直播下载app AVBOBO下载app 暖暖直播app下载 花样视频app下载 宅男之家下载app 午夜直播间app下载 茄子视频app下载 樱桃视频app下载 番茄直播下载app 云上花下载app 青草视频app下载 享爱app下载 七秒鱼直播app下载 烟花直播app下载 浪浪视频下载app 恋人直播下载app 梦鹿直播下载app 月光直播app下载 火爆社区app下载 野花视频下载app 食色app下载 朵朵直播app下载 宅男之家下载app 咪哒app下载 豆奶抖音短视频下载app 麻豆传媒直播下载app 葫芦娃视频下载app 快播破解app下载 91视频app下载 富二代f2短视频app下载 水蜜桃下载app 快猫短视频app下载 成版人茄子视频下载app 久草视频下载app 水蜜桃下载app 么么直播下载app 快播破解app下载 花秀神器app下载 小米粒直播app下载 fi11含羞草下载app 比心下载app 花心社区下载app 橘子视频下载app 午夜神器app下载 午夜直播间下载app 蝶恋花直播app下载 含羞草app下载 红高粱直播app下载 七仙女直播下载app 蜜桃直播app下载 小宝贝直播app下载 抖阴下载app 后宫视频app下载 套路直播下载app 圣女直播下载app视频免费最新 快播破解app下载 音色短视频下载app 小酒窝直播下载app 黄瓜下载app 野花视频app下载 小蝌蚪下载app Avboboapp下载 月亮直播下载app 荔枝下载app 猛虎视频下载app 月光宝盒直播下载app 榴莲视频app下载 花心app下载 荔枝下载app 色秀直播下载app 望月下载app 金鱼直播app下载 内裤直播下载app视频免费最新 小狐仙app下载 男人本色西瓜视频下载app 夜狼直播app下载 心上人直播下载app 成人直播下载app 青青草下载app 夜巴黎直播下载app 香蕉视频下载app 快猫app下载 音色短视频下载app视频免费最新 小草视频app下载 富二代f2短视频app下载 梦鹿直播app下载 美岁直播app下载 猫咪软件app下载 橘子直播下载app 猛虎视频下载app 茄子app下载 美梦视频下载app视频免费最新 浪浪视频下载app视频免费最新 性福宝下载app 黄瓜直播app下载 大秀直播app下载 番茄视频app下载 梦鹿直播app下载 金鱼直播下载app 美梦视频app下载 成版人短视频app下载 恋人直播下载app 可乐视频下载app视频免费最新 小小影视下载app 左手视频app下载 麻豆传媒下载app fi11含羞草下载app 富二代f2抖音app下载 春水堂视频app下载 富二代f2抖音下载app Huluwa下载app 午夜直播app下载 丝瓜app下载 大番号下载app 酷咪直播app下载 杏趣直播下载app AVnightapp下载 菠萝蜜app下载 骚虎直播下载app 花心视频下载app