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Real World Deep Learning: Neural Networks for Smart Crops

@machinelearnbot

To produce high-quality food and feed a growing world population with the given amount of arable land in a sustainable manner, we must develop new methods of sustainable farming that increase yield while minimizing chemical inputs such as fertilizers, herbicides, and pesticides. I and my colleagues are working on a robotics-centered approaches to address this grand challenge. My name is Andres Milioto, and I am a research assistant and Ph.D. student in robotics at the Photogrammetry and Robotics Lab (http://www.ipb.uni-bonn.de) Together with Philipp Lottes, Nived Chebrolu, and our supervisor Prof. Dr. Cyrill Stachniss we are developing an adaptable ground and aerial robots for smart farming in the context of the EC-funded project "Flourish" (http://flourish-project.eu/), where we collaborate with several other Universities and industry partners across Europe. The Flourish consortium is committed to develop new robotic methods for sustainable farming that aim at minimizing chemical inputs such as fertilizers, herbicides, and pesticides in order to reduce the side-effects on our environment.


AI for Good: How advanced crop intelligence can help solve food production challenges

#artificialintelligence

Farmers spend nearly half of their operational budgets on agrochemicals such as herbicides and pesticides. Unfortunately, they usually apply these to entire fields at a time, which generates high chemical costs and decreases the efficacy of the chemicals. Such widespread application of chemicals harms the environment, endangers human health, and increases the likelihood of chem ical-resistance in weeds, pests, and diseases. And, even with that damaging widespread application, loss to weeds, pests and diseases can range from 20-50%. But manually scouting and sampling to determine the locations of these problems is time-consuming and costly, and cannot easily account for the enormous variety of factors that affect crops.


Machine Learning in Agriculture: Applications and Techniques

#artificialintelligence

Recently we have discussed the emerging concept of smart farming that makes agriculture more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is Machine Learning -- the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments. Machine learning is everywhere throughout the whole growing and harvesting cycle. It begins with a seed being planted in the soil -- from the soil preparation, seeds breeding and water feed measurement -- and it ends when robots pick up the harvest determining the ripeness with the help of computer vision.


Machine Learning in Agriculture: Applications and Techniques

#artificialintelligence

Recently we have discussed the emerging concept of smart farming that makes agriculture more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is Machine Learning -- the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments. Let's discover how agriculture can benefit from Machine Learning at every stage: Our favorite, this application is so logical and yet so unexpected, because mostly you read about harvest prediction or ambient conditions management at later stages. Species selection is a tedious process of searching for specific genes that determine the effectiveness of water and nutrients use, adaptation to climate change, disease resistance, as well as nutrients content or a better taste.


Farm Robotics are Taking a Giant Automated Leap Forward

#artificialintelligence

In today's industrial environment, robots appeal to several industries because they help ease labor concerns--specifically, an aging workforce and the potential to increase the efficiency of work output. This is no different in the agricultural world. Introducing robots into the fieldwork would help reduce labor concerns that are currently being experienced in both the U.S. and in Europe. Robots and new technology would also help alleviate the increase need of precision work and limitations brought on by new chemical and natural resources farming standards. However, the delicate nature of the work makes it difficult to introduce traditional industrial robots.