news details |
|
|
| Artificial intelligence in agriculture | | | Dr. Parveen Kumar,
Dr. R. K. Arora,
Dr. Pawan Kumar Sharma
The word 'Artificial intelligence' was coined by John Mc Carthy in 1950. Since then it has travelled a long way being exploited in one or the other way to serve the mankind in the best possible way. Agriculture is both a major industry as well as foundation of the economy. The approximate value added by the agricultural industry was estimated at just below 1 percent of the US GDP in 2016. The US Environmental Protection Agency (EPA) estimates that agriculture contributes roughly $330 billion in annual revenue to the economy. Agriculture plays a vital role in India's economy too. Over 58 percent of the rural households depend on agriculture as their principal means of livelihood. Agricultural exports constitute 10 percent of the country's exports and are the fourth-largest exported principal commodity category in India. According to the Department of Industrial Policy and Promotion (DIPP), the Indian agricultural services and agricultural machinery sectors have cumulatively attracted Foreign Direct Investment (FDI) equity inflow of about $2.45 billion and the food processing sector has attracted around $7.81 billion during April 2000 to June 2017. The increased FDI along with some pro farmer government initiatives, the agriculture sector is now equipping itself to leverage technology for better crop yield. Many technology companies and startups have emerged in the past few years with targeted agri-based solutions that benefit the farmers.
In the context of leveraging agriculture with technology, artificial intelligence has emerged as an important tool for performing various agricultural related tasks and accomplishing them with great efficiency. Artificial intelligence is a sort of machine learning where we try to induce a sense of perception, learning, reasoning and understanding in machines or robots. Now various companies have developed agricultural robots which can handle all the essential agriculture related operations like harvesting crops at a higher volume and faster pace than human laborers. In this regard the crop and soil Monitoring is done with the help of censors and by leveraging computer vision and deep-learning algorithms to process data captured by drones and/or software-based technology to monitor crop and soil health. In predictive agricultural analytics, various artificial intelligence and machine learning tools are being used to predict the optimal time to sow seeds, get alerts on risks from pest attacks, and more. Various machine learning models are being developed to track and predict various environmental impacts on crop yield such as weather changes. Many companies have also now come up with Supply Chain Efficiencies. These Companies are using real-time data analytics on data-streams coming from multiple sources to build an efficient and smart supply chain.
Today weed management is an important aspect for healthy crops and consequently obtaining higher crop yields. An estimated 250 species of weeds have become resistance to herbicides. A research study conducted by the Weed Science Society of America on the impact of uncontrolled weeds on corn and soybean crops, reported an annual loss of $43 billion to farmers. The ability to control weeds is a top priority for farmers and an ongoing challenge as weeds have become more herbicide resistant. The companies have now come up with automation and robotics to help farmers find more efficient ways to protect their crops from weeds. Blue River Technology has developed a robot called 'See and Spray' which reportedly leverages computer vision to monitor and precisely spray weeds on cotton plants. This precision spraying has helped prevent herbicide resistance. According to its website, the company claims that its precision technology eliminates 80 percent of the volume of chemicals normally sprayed on crops and can reduce herbicide expenditure by 90 percent. In a country like United States where it has been estimated that over 1 billion pounds of pesticides are used annually, reduction of herbicide expenditure through the use of robotics matter a lot. To reduce the challenges in labour force, automation is also emerging as an important tool to address this issue. The industry is also projected to experience a 6 percent decline in agricultural workers from 2014 to 2024. Harvest CROO Robotics has developed a robot to help strawberry farmers pick and pack their crops. Lack of laborers has reportedly led to millions of dollars of revenue losses in key farming regions such as California and Arizona. In the Hillsborough County, Florida region which has been described as the "nation's winter strawberry capital," between 10,000 and 11,000 acres of strawberries are typically harvested in a season. Harvest CROO Robotics claims that its robot can harvest 8 acres in a single day and replace 30 human laborers.
Time of sowing is very critical component in ensuring a better yield. Towards this, the International Centre for Research on Semi Arid Tropics 'ICRISAT' has in collaboration with Microsoft has developed an Artificial intelligence Sowing App powered by Microsoft Cortana Intelligence Suite including Machine Learning and Power BI. This app sends sowing advisories to participating farmers on the optimal date to sow. It uses artificial intelligence to inform the farmers in selected district of Hyderabad about the right sowing date which is very critical to ensure that farmers harvest a good crop. When the farmers are informed about the right date of sowing, it prevents them from loss which they would have incurred due to costs of seeds, as well as the fertilizer applications. This information about timely sowing has already resulted in 30 per cent increase in yield of the farmers receiving the messages. The most interesting thing is that this is very much affordable. Farmer do not have to install any sensors in their fields or incur any capital expenditure. They need to only have a smart feature phone capable of receiving text messages. |
|
|
|
|
|
|
|
|
|
|
|
|
| |
| |
|
|
|
|
 |
|
|
