Agriculture is the fundamental need of Human beings. The agricultural revolution is one of the main reasons for the formation of human civilizations. Growing food by ourselves granted us leisure time to pursue art and science, which ultimately resulted in the birth of modern society.
With extensive use of resources like land, fertilizers and water have increased farming capacity exponentially but stressed our landscapes. By some estimates, world food production needs to increase by seventy percent to fulfill global demand. It is up to technology to make farming more efficient and meet rising global demands.
Smart agriculture is a farming management concept that guides farmers to transform and reorient agricultural systems to guarantee food security in an ever-changing climate. The main approach of smart agriculture is to increase agricultural productivity, profits and reduce greenhouse gas (GHG) emissions.
The smart agriculture market is driven by the increasing uncertainty of the global climate, yet the high cost of implementing it might hamper its demand.
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IoT in Agriculture
Today’s farmers have access to GPS technology, soil scanning, water, sunlight, humidity, temperature management, automatic water sprinkling, precision agriculture, data management, and IoT technologies.
Data and IoT-based smart farming is the future of agriculture. Farmers can record real-time data of condition of the soil, the soil moisture, and the amount of sunlight received by the plants with sensors on tractors and also on the fields. The analysis of data collected will give farmers the insight they need to keep feeding the world in a smarter way.
Machine learning and Analytics
AI and machine learning can provide reliable information to farmers to decide which seeds to buy, how many to plant, and which fertilizers or nutrients will produce the best outcome in different regions. Machine learning can predict which traits and genes will be best for crop production at a particular time of the year, giving farmers the best breed for their location and climate. Machine learning techniques that use satellite data to distinguish between crops is providing valuable information for crop insurance and commodity markets.
Crop spraying: Drones scan the ground and spray fertilizers five times faster than tradition machinery.
Planting seeds: Drone and robotic planting systems shoot seeds and nutrients into the soil, providing all nutrients necessary for growing crops by which planting costs have decreased by over eighty-five percent.
Crop scanning: Drones can scan a crop using both visible and near-infrared light to track changes in plants and indicate their health and alert farmers in case of disease and prompt action.