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Top 4 Applications of AI and Machine Learning in Agriculture


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.

A Glance at the Agriculture of the Future: Farm Automation


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.

Smart Farming: IoT in agriculture - IoT India Magazine


Internet of things has been proving its mettle across the industries such as banking, retail, tele companies, manufacturers and more. Amidst the various industries, the one sector it is quickly catching up with is, the agriculture. With the concept of smart farming and digitization, it is gaining popularity like never before and is coming with the potential to offer high precision crop control, data collection and automated farming techniques. A forecast by the food and agriculture arm of the United Nations (FAO) says that the food production worldwide should see a 70% increase by 2050 to cater the ever growing population. And the experts believe that IoT could play a crucial role in meeting this need.

Machine Learning in Agriculture: Applications and Techniques


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.

The Global Artificial Intelligence (AI) in Agriculture Market Analysis projects the market to grow at a significant CAGR of 28.38% during the forecast period from 2019 to 2024


Key Questions Answered in this Report: • What is the estimated global artificial intelligence in agriculture market size in terms of value during the period 2018-2024? Global Artificial Intelligence (AI) in Agriculture Market Forecast, 2019-2024 The Global Artificial Intelligence (AI) in Agriculture Market Analysis projects the market to grow at a significant CAGR of 28.38% during the forecast period from 2019 to 2024. The reported growth in the market is expected to be driven by the increasing need to optimize farm operation planning, growing demand to derive insights from emerging complexities of data-driven farming, and rising development of autonomous equipment in agriculture. Artificial intelligence has emerged to be a strong driving force behind the growth of data-driven farming.Regions and countries where agriculture is the major source of livelihood and sustenance, the artificial intelligence technology has led to greater profitability in the farms of those economies. The reduction in expenditure and resultant positive RoI with AI's integration in farm equipment and operations has even reached above 30% in a few countries.