The Internet of Things (IoT) in Agriculture: IoT Solutions for Smart Farming - Digiteum

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Today's agriculture is in a race. Farmers have to grow more product in deteriorating soil, declining land availability and increasing weather fluctuation. IoT-enabled agriculture allows farmers to monitor their product and conditions in real-time. They get insights fast, can predict issues before they happen and make informed decisions on how to avoid them. Additionally, IoT solutions in agriculture introduce automation, for example, demand-based irrigation, fertilizing and robot harvesting.


The Internet of Things (IoT) in Agriculture: IoT Solutions for Smart Farming - Digiteum

#artificialintelligence

Today's agriculture is in a race. Farmers have to grow more product in deteriorating soil, declining land availability and increasing weather fluctuation. IoT-enabled agriculture allows farmers to monitor their product and conditions in real-time. They get insights fast, can predict issues before they happen and make informed decisions on how to avoid them. Additionally, IoT solutions in agriculture introduce automation, for example, demand-based irrigation, fertilizing and robot harvesting.


Glossary: Smart farming

ZDNet

This article was originally published on TechRepublic. Aerial imagery: Photos taken from the air, often with UAVs in smart farming. Used to assist farmers to determine the condition of a field. It is the integrated internal and external networking of farming operations as a result of the emergence of smart technology in agriculture. Agro-chemicals: Chemicals used in agriculture, which include fertilizers, herbicides, and pesticides.


Data Science in the Indian Agriculture Industry

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Agriculture is the backbone of the Indian economy, but the industry currently needs more support than any other. India is a country of over a billion people in population, out of which, over 70% of the population lives in the rural areas. With 40% of the country's workforce, agriculture is a major industry and an influencer of the Indian economy. Despite this, its contribution to the $2.3 trillion economy is just a meager 16% of the entire GDP. Agriculture in India lacks institutional attention, support from banks in terms of loans and farmer welfare schemes, and suffer from a myriad of disasters like depleting groundwater levels in rural areas, climate change, unpredictable monsoon or lack of it, droughts, floods, unfair price fixing policies of produce, migration of farmers towards the cities in search of better paying jobs, and more.


Machine Learning in Agriculture: Applications and Techniques

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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.