Wendy Gonzalez is the CEO of Sama, the provider of accurate data for ambitious AI. The population has doubled since 1974, and today, there are 8 billion humans living on this planet. While population growth has slowed, at the current rate, we should reach 9 billion in 15 years. What does it take to feed all these people? When John McCarthy coined the term "artificial intelligence" in 1955 as "the science and engineering of making intelligent machines," could he have imagined technology's effect on our ever-growing food supply needs?

MILWAUKEE -- Agriculture is among the last major industries to become digitized. It's doesn't come as a major surprise, seeing as how off-road, rural environments are more challenging than roadway systems or manufacturing floors. However, as the connectivity gap continues to close, there is tremendous opportunity to capture data that can ultimately lead to transformative technologies like artificial intelligence (AI). "To put it as simply as possible, AI allows computer systems to complete tasks that are normally performed by humans," said Mark Kuehn, OEM sales manager for North America at Trimble. Given that definition, AI could mean everything from cognitive tasks like data analytics and forecasting to physical tasks like spraying weeds and picking produce.

Robotics is rapidly gaining traction, and many industries have accepted robots in their businesses. However, some path breaking innovation is helping robotics expand its outreach beyond business. The concept of robots in business has worked quite well and is still opening new avenues for not only big but also for small businesses. From industrial automation to Robotics as a Service (RaaS), robots are on the verge of becoming an indispensable part of our lives. Collaborative robots and piece picking robots have proved to be a success in the retail industries and other physical movement intensive companies.

Smart farming using AI and IoT is no longer a distant dream, smart farms are here to stay thanks to amazing advancements in AI and IoT devices. Over the past decades, the agriculture sector has undergone significant changes. Today, it's possible to grow plants even in the most hostile climatic regions. Crops are more resistant to insects, weeds, and climate change than ever before. Lastly, it's possible to breed high-yielding farm animals. But despite all these advancements, a large population of the world is still undernourished.

Technological advances are bringing change to a great number of industries, and the agriculture industry is no exception. Farms are slowly starting to see increased adoption of practices based on technologies such as artificial intelligence, cloud computing, the Internet of Things (IoT), and robotics. The adoption of such technologies into the traditional farming practices as we know them is referred to as smart farming or farm automation. Let's have a look at what farm automation is exactly and how it can help farmers tackle a number of challenges in today's agricultural sector. Farm automation specifically focuses on applying data and information technologies for the optimization of production processes of complex farming systems as well as the quality of the food.

Agriculture and farming are some of the oldest and most important professions in the world. It plays an important role in the economic sector; especially in India, where agriculture has been the primary occupation for ages. The global population is expected to reach more than nine billion by 2050 which will require an increase in agricultural production by 70% to fulfill the demand. In this article, guest author Melanie writes about the applications of AI and Machine Learning in agriculture. The success of a business or industry is dependent on several factors, one of which is effective decisions.

Robots in agriculture are becoming increasingly used by the industry today. An example would be the multiple analytics and machine learning tools used in smart farming to help with predicting harvests. One of these tools, agriculture robots, are normally used collaboratively (known as cobots). These robots possess mechanical arms and make harvesting much easier for farmers. Compared to traditional industrial robots and machinery, cobots are designed to work alongside human employees, giving manufacturers the benefits of both robots and humans combined.

Facial recognition technology is rapidly becoming ubiquitous, used in everything from security cameras to smartphones. But in the near future, humans may not be the only ones to be digitally captured. Researchers are training forms of artificial intelligence to recognize individual animals by their faces alone -- and even discern their emotional state just by reading their expressions. Much of the research into animal facial expressions has focused on species like dogs and horses. But some of the most cutting-edge work is aimed at an unlikely subject: the farmed hog.

We will show how a team of researchers applied JADBio’s Automated Machine Learning (AutoML) platform to predict potatoes' susceptibility to bruising and also its potential for coloration during chip/crisp processing. The aim was to differentiate between potatoes that would be less prone to bruising from those that would more easily bruise during mechanical handling. Another goal was to successfully predict the potatoes’ potential susceptibility to acrylamide formation during chip/crisp processing due to the Maillard reaction. In this webinar series, Aris Karanikas (Business Development Officer) and Vincenzo Lagani (VP of Bioinformatics) at JADBio will demonstrate the advanced capabilities of AutoML to assist researchers and agronomists in data analysis. They will explain how to apply the JADBio platform based on real-life agricultural case-studies. Artificial intelligence (AI) and application of machine learning models are currently trending in the agriculture industry, and you will learn how it can help you to make better analytic decisions and improve your data interpretation efficiency. By attending this webinar, you will discover: - How you can analyze and classify your potato samples, without extensive data science knowledge - Discover which specific features play a role in high quality potatoes, along with their relative strength as predictors - Understand how relevant sets of equivalent predictors can also affect the desired result - How to apply your model on all future potato samples - How AutoML can help the agriculture industry in more efficient seed production, breeding, and many other sectors of the industry Who is this Webinar for: - Researchers - Agronomists - Farmers and anyone who needs to discover how they can utilize machine learning to predict crop performance, without the need to learn data science or acquire programming skills. Take-away: All attendees will receive a fully functional monthly licence (free of charge) for JADBio AutoML

Despite a growing population, now predicted to reach 9.6 billion by 2050, the agriculture industry must rise to meet demand, regardless of environmental challenges like unfavorable weather conditions and climate change. To meet the needs of that growing population, the agriculture industry will have to adopt new technologies to gain a much-needed edge. New agricultural applications in smart farming and precision farming through IoT will enable the industry to increase operational efficiency, lower costs, reduce waste, and improve the quality of their yield. So, what is smart farming? Smart farming is a capital-intensive and hi-tech system of growing food cleanly and sustainable for the masses.