A new generation of farmers is turning to technologies such as the internet of things (IoT) and machine learning to automate agricultural production, alleviating the need to toil on the land while keeping a watchful eye on their crops. Joining their ranks is Phoebe Xie, director and co-founder of Singapore-based agrotechnology startup AbyFarm. Teaming up with technology service provider SPTel, Xie is building a smart hydroponics farm in a greenhouse that uses a plethora of IoT sensors, including video cameras, to keep the farm humming around the clock. "To run a self-regulating farm at optimal temperature with optimal water and nutrient supply, and to control the quality and taste of vegetables and fruits, we need IoT sensors," she said, adding that the farm and its sensors collect and monitor thousands of data points, including humidity and temperature. With the data and in certain environmental conditions, processes and actions are automatically triggered to protect crops from the elements.

Hello, today I'd like to explain briefly how we use artificial intelligence to count sunflower seeds in a photo taken with a mobile device. Agenda: 1. Business needs 2. Data preparation 3. Model structure 4. Used libs and tools 5. Results 6. Error analysis 7. Fails/Hypotheses 8. Conclusion 9. References Fortunately for me, I am working at Kernel. Where I am developing Computer Vision (CV) and other models to solve business problems and challenges. One of them is to count seeds on sunflower.

Agremo is one of the fastest growing startup in Europe that develops intuitive agricultural sensing and drone analysis platform for drone operators, growers, and agronomists. For several years now, AgroTech startups from all around the world, have been making major and significant changes in the agricultural sector. Their goal is to help make this industry much more digitized and more modern. With drones which are sensors equipped for scanning land and subsequently processing large amounts of data, users get powerful tools that take their business to a whole new level. Among them is Agremo, a Serbian startup backed by South Central Ventures and StartLabs, which has developed an advanced AI platform that allows farmers to analyze fields and seedlings using drone photos.

Did you know you could use drone data in agriculture to lower costs and improve crop performance? If yes, chances are you've already heard about Agremo. We've just released a new version of our app and are excited to show you what you can do with drone data in agriculture today. There are many ways in which drone data can help you improve activities on the field, and the different analyses are by far the most important factor. Essentially, you make a drone map of your field and request the according analysis you need.

When Geoffrey von Maltzahn was first pitching farmers to try out his startup's special seeds, he sometimes told them, half-acknowledging his own hyperbole, that "if we're right, you shouldn't just see results in the field, you should be able to see them from outer space." As the co-founder of a company called Indigo Ag, von Maltzahn was hawking a probiotic that he hoped would increase their crop yields dramatically. "I never thought we'd ever actually test that idea," he says. In the three years since Indigo began selling naturally occurring organisms such as bacteria and fungi, spray-coated onto seeds, the company has grown to become perhaps the most valuable agtech company in the world. Pitchbook, for example, estimates Indigo's value at $3.5 billion.

When you think about artificial intelligence (AI), you probably don't imagine using it for a farm. But you should: this week, IBM is bringing data and AI together with the global release of the Watson Decision Platform for Agriculture to help growers and enterprises make better decisions. This new platform is an innovation that draws upon IBM's most advanced capabilities in AI, analytics, IoT, Cloud, and weather to create a suite of solutions that span the farm-to-fork ecosystem. Farming has always been a complex undertaking that requires growers to manage an interconnected web of pre-season and in-season decisions while at the mercy of mother nature. With the explosion of data from farm equipment, environmental sensors, and remote input, it's impractical to rely on intuition or traditional technology to understand what drives variation in yield or provide guidance to growers.

A new lightweight, low-cost agricultural robot could transform data collection and field scouting for agronomists, seed companies and farmers. The TerraSentia crop phenotyping robot, developed by a team of scientists at the University of Illinois, will be featured at the 2018 Energy Innovation Summit Technology Showcase in National Harbor, Maryland, on March 14. Traveling autonomously between crop rows, the robot measures the traits of individual plants using a variety of sensors, including cameras, transmitting the data in real time to the operator's phone or laptop computer. A custom app and tablet computer that come with the robot enable the operator to steer the robot using virtual reality and GPS. TerraSentia is customizable and teachable, according to the researchers, who currently are developing machine-learning algorithms to "teach" the robot to detect and identify common diseases, and to measure a growing variety of traits, such as plant and corn ear height, leaf area index and biomass.

A new lightweight, low-cost agricultural robot could transform data collection and field scouting for agronomists, seed companies and farmers. The TerraSentia crop phenotyping robot, developed by a team of scientists at the University of Illinois, will be featured at the 2018 Energy Innovation Summit Technology Showcase in National Harbor, Maryland, on March 14. Traveling autonomously between crop rows, the robot measures the traits of individual plants using a variety of sensors, including cameras, transmitting the data in real time to the operator's phone or laptop computer. A custom app and tablet computer that come with the robot enable the operator to steer the robot using virtual reality and GPS. TerraSentia is customizable and teachable, according to the researchers, who currently are developing machine-learning algorithms to "teach" the robot to detect and identify common diseases, and to measure a growing variety of traits, such as plant and corn ear height, leaf area index and biomass.

A group of maize farmers stands huddled around an agronomist and his computer on the side of an irrigation pivot in central South Africa. The agronomist has just flown over the pivot with a hybrid UAV that takes off and lands using propellers yet maintains distance and speed for scanning vast hectares of land through the use of its fixed wings. The UAV is fitted with a four spectral band precision sensor that conducts onboard processing immediately after the flight, allowing farmers and field staff to address, almost immediately, any crop anomalies that the sensor may have recorded, making the data collection truly real-time. In this instance, the farmers and agronomist are looking to specialized software to give them an accurate plant population count. It's been 10 days since the maize emerged and the farmer wants to determine if there are any parts of the field that require replanting due to a lack of emergence or wind damage, which can be severe in the early stages of the summer rainy season.