AI Farming: Bots are now totally capable of growing our food. In fact, it is already happening. We can look forward to a future where we grow potatoes in the Metaverse while bots grow the real deal in the field. What does the future hold for farming? From here, the road ahead is exciting.

Artificial intelligence (AI) is on the cusp of driving an agricultural revolution, and helping confront the challenge of feeding our growing global population in a sustainable way. But researchers warn that using new AI technologies at scale holds huge risks that are not being considered. Imagine a field of wheat that extends to the horizon, being grown for flour that will be made into bread to feed cities' worth of people. Imagine that all authority for tilling, planting, fertilising, monitoring and harvesting this field has been delegated to artificial intelligence: algorithms that control drip-irrigation systems, self-driving tractors and combine harvesters, clever enough to respond to the weather and the exact needs of the crop. Then imagine a hacker messes things up.

Artificial intelligence (AI) is on the cusp of driving an agricultural revolution, and helping confront the challenge of feeding our growing global population in a sustainable way. But researchers warn that using new artificial intelligence technologies at scale holds huge risks that are not being considered. Imagine a field of wheat that extends to the horizon, being grown for flour that will be made into bread to feed cities' worth of people. Imagine that all authority for tilling, planting, fertilizing, monitoring, and harvesting this field has been delegated to artificial intelligence: algorithms that control drip-irrigation systems, self-driving tractors, and combine harvesters, clever enough to respond to the weather and the exact needs of the crop. Then imagine a hacker messes things up. A new risk analysis, published recently in the journal Nature Machine Intelligence, warns that the future use of artificial intelligence in agriculture comes with substantial potential risks for farms, farmers, and food security that are poorly understood and under-appreciated.

Imagine a field of wheat that extends to the horizon, being grown for flour that will be made into bread to feed cities' worth of people. Imagine that all authority for tilling, planting, fertilizing, monitoring and harvesting this field has been delegated to artificial intelligence: algorithms that control drip-irrigation systems, self-driving tractors and combine harvesters, clever enough to respond to the weather and the exact needs of the crop. Then imagine a hacker messes things up. A new risk analysis, published today in the journal Nature Machine Intelligence, warns that the future use of artificial intelligence in agriculture comes with substantial potential risks for farms, farmers and food security that are poorly understood and under-appreciated. "The idea of intelligent machines running farms is not science fiction. Large companies are already pioneering the next generation of autonomous ag-bots and decision support systems that will replace humans in the field," said Dr. Asaf Tzachor in the University of Cambridge's Center for the Study of Existential Risk (CSER), first author of the paper.

Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace. Tuning the combine harvesters and improving the machine performance can dramatically minimize the wastes during harvesting, and it is also beneficial to machine maintenance. Literature includes several soft computing, machine learning and optimization methods that had been used to model the function of harvesters of various crops. Due to the complexity of the problem, machine learning methods had been recently proposed to predict the optimal performance with promising results. In this paper, through proposing a novel hybrid machine learning model based on artificial neural networks integrated with particle swarm optimization (ANN-PSO), the performance analysis of a common combine harvester is presented. The hybridization of machine learning methods with soft computing techniques has recently shown promising results to improve the performance of the combine harvesters. This research aims at improving the results further by providing more stable models with higher accuracy.

Driverless tractors, combine harvesters and drones have grown a field of crops in Shropshire in a move that could change the face of farming. The autonomous vehicles followed a pre-determined path set by GPS to perform each task, while the field was monitored by scientists using self-driving drones. The project, called hands Free Hectare, began with autonomous tractors drilling channels to precise depths for the barley seeds to be planted. The tractor was also used to plant seeds and spray fungicides, herbicides, and fertilisers. An automated combine harvester then harvested the field of barley.

Although the quest for Artificial Intelligence (AI), equipping trading algorithms with human qualities such as self-learning, continues to fascinate, it will be the explosion of the Internet of Things that will soon re-energize trading in capital markets. The Internet of Things (IoT) is rapidly growing through the addition of sensors to machines that allow them to "feel." Once they are equipped with feelings-- particularly sight, sound and touch-- machines can behave more intelligently, for example optimizing operations to use less fuel or predicting when they need maintenance. However, an interesting side effect is that the data from the IoT could be a new source of "insider" data for trading firms. For example, if combine harvesters (accessorized with sensors) signal a bumper wheat cropin the U.S. grain belt, traders can take advantage of this information before the crop report is issued.