Revolution of Big Data in Precision Farming
- yogeshpawar1828
- Dec 6, 2019
- 2 min read
I was reading through the NITI Ayog report on National Strategy for Artificial Intelligence. Found quite interesting to know the facts about agriculture conditions in India and how AI can help improve it.
Government is putting in lots of efforts but still could not bring in the desired improvements due to various factors like land degradation, use of chemical fertilizers, dropping of water tables and poor resource utilization, with the production quantum and productivity still being quite low.
India as compared to other countries receives very good rainfall, but due to inefficient water management there is continuous exploitation of ground water . The report says that agriculture consumes 89% of extracted ground water to irrigate just one-third of gross cropped area.
Not only this in market non-existent functional end-to-end agriculture value chains have caused the price realization for farmers to remain low.
With AI and Big data technologies introduction in agriculture , the farmers can achieve significant increase in productivity. Many technology companies like John Deere are helping develop intelligent solutions to marry the smart technologies with mechanized farming thereby driving precision resulting in higher yield.
Today we have lot of sensor data that we get from machines and field. For example, by getting soil condition data as temperature, moisture coupled with weather forecast data we can exactly tell farmer when to start seeding.
Image recognition and deep learning models along with data signals from remote satellites, as well as local image capture in the farm, have made it possible for farmers to take immediate actions to restore soil health.
Microsoft in collaboration with ICRISAT, have developed an AI Sowing App powered by Microsoft Cortana Intelligence Suite including Machine Learning and Power BI. The app sends sowing advisories to participating farmers on the optimal date to sow.
Image classification tools combined with remote and local sensed data can help in areas of weed removal, early disease identification, produce harvesting and grading.
Berlin-based agricultural tech startup PEAT has developed a deep learning application called Plantix that reportedly identifies potential defects and nutrient deficiencies in the soil.
Also Blue River Technology has designed and integrated computer vision and machine learning technology that enables farmers to reduce the use of herbicides by spraying only where weeds are present, optimizing the use of inputs in farming – a key objective of precision agriculture.
NITI Aayog and IBM have partnered to develop a crop yield prediction model using AI to provide real time advisory to farmers. IBM’s AI model for predictive insights to improve crop productivity, soil yield, control agricultural inputs and early warning on pest/disease outbreak will use data from remote sensing (ISRO), soil health cards, IMD’s weather prediction and soil moisture/temperature, crop phenology etc. to give accurate prescriptions to farmers
The future is not far where we will see entire farming process automated by applying Artificial Intelligence to produce sufficient yield to feed 10 billion world population.
Yogesh Pawar
Ref : NITI Ayog report
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