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Modern Agriculture

Modern agricultural systems have been developed with two related goals in mind: to obtain the highest yields possible and to get the highest economic profit possible. In pursuit of these goals, six basic technologies have come to form the backbone of production: GIS in Agriculture, Satellite Imagery, Automation of Data, Artificial Intelligence, Mobile Application. Each technology is used for its individual contribution to productivity, but when they are all combined in a farming system each depends on the others and reinforces the need for using the others. The work of agronomists, specialists in agricultural production, has been key to the development of these practices.

Top 5 Technologies in Modern Agriculture

GIS in Agriculture

GIS applications play an important role in the production of crops, both locally and across the globe. Through assisting farmers in increasing production, reducing costs, and providing an effective means of managing land resources, GIS has become an increasingly invaluable resource (AABSyS IT, 2018). Its applications come in a variety of forms, including precision farming, drone and satellite technologies and the capabilities of Geographical Information Systems themselves.

Agricultural Geographic Information Systems (AGIS) can map not only topography and crop health, but help solve wider economic issues in municipalities and urban centers that may stem from rural farming practices.


Satellite Imagery

Predicting yields, as well as conducting almost real-time field monitoring, with a view to detect a variety of threats with satellite data in service has never been so easy.

The sensors are able to give imagery in various spectra, allowing for the application of numerous spectral indices, such as the Normalized Difference Vegetation Index (NDVI). NDVI allows for the detection of vegetation content, the amount of wilting plants, and overall plant health. Next is the Canopy Chlorophyll Content Index (CCCI) that helps with nutrient application. Then, the Normalized Difference RedEdge (NDRE) detects Nitrogen content. And lastly, the Modified Soil-Adjusted Vegetation Index (MSAVI) is designed to minimize soil background impact at the earliest developmental stages of plants; the list goes on.

Artificial Intelligence

Farms produce hundreds of thousands of data points on the ground daily. With the help of AI, farmers can now analyze a variety of things in real time such as weather conditions, temperature, water usage or soil conditions collected from their farm to better inform their decisions. For example, AI technologies help farmers optimize planning to generate more bountiful yields by determining crop choices, the best hybrid seed choices and resource utilization.

AI systems are also helping to improve harvest quality and accuracy — what is known as precision agriculture. Precision agriculture uses AI technology to aid in detecting diseases in plants, pests, and poor plant nutrition on farms. AI sensors can detect and target weeds and then decide which herbicides to apply within the right buffer zone.

Through the use of agricultural AI and cognitive technologies, farms across the world are able to run more efficiently to produce more efficiently.

Automation of Data

Technology is transforming our food chain, with some of the most important innovation occurring in the rise of precision farming. Together, artificial intelligence (AI) and automation are revamping the agriculture industry, helping farmers operate efficiently and in new ways.

Automation and farming make an excellent match for addressing needs the agriculture sector may face in the coming years.

satellite imges agriculture
Farmer in rice field with laptop


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