Artificial Intelligence and smartfarming

By Author  |  Published: 21st Oct 2017  12:36 amUpdated: 21st Oct 2017  12:37 am
Artificial Intelligence and Smartfarming

According to the UN Food and Agricultural Organisation (FAO), the world population would be 9.2 billion by the year 2050. Hence, the agriculture sector has to feed 2 billion more mouths within the next 33 years. Could Artificial Intelligence be just what the doctor ordered?

According to a paper published by Imperial Journal of Interdisciplinary Research, here are a few use cases of Artificial Intelligence in farming:

Chatbot: Built using machine learning and natural language processing, chatbots, especially those which are trained to deliver in regional languages of Indian farmers, can really improve farmer productivity. They can provide context sensitive assistance to farmers who can have a wealth of questions answered. The user will be asked to enter data like crop, location and problems the person is facing to map the solution to a standard predefined in a database. These bots can be deployed on websites, apps or messaging services like those of Facebook.

Agri-E-calculator: The agri-e-calculator as a smart application that helps the smart farmer to choose the most suitable crop based on several dependency factors. Once the farmer chooses the desired crop, the application would ask for input of various dependency factors related to that crop. The application then provides useful estimates of fertilizers cost/quantity, water, seeds, cultivation equipment cost, crop yield along with extrapolated market price at the harvest time and its profitability. This could fundamentally change the way a farmer runs his business.

Crop care service: From the moment seeds are planted to the time they are harvested, crops can be subject to monitoring by a reliable Internet of Things (IoT) sensor network. The resulting data collected is processed by Artificial Intelligence engines which in turn alert the farmer on their smartphone about any corrective measures they need to take. This is like an end-to-end monitoring service that works 24X7.

Price prediction and market guidance: While their efforts to produce crop yield remain constant, farmers are exposed to the vagaries of market in the end. Notably, there is a need for them to avoid releasing crops into the market when their prices drop. Based on statistical data collected, a predictive price and demand information is shared with the farmers. AI engines crunch the sophisticated data inputs and guide the farmer in a way that has an impact on their bottom line.

Driverless tractors: According to Sputniknews, driver-less tractors can achieve the trifecta of reducing the number of manual labourers on the field, using a vehicle which is potentially 20% lower in price compared to traditional tractors and avoiding the manual handling of hazardous substances like insecticides and pesticides. It would be interesting to see if the Indian Government change its stance on autonomous vehicles in the near future.

What is the bigger picture?

According to Vivek Rajkumar, CEO of an agritech startup, India holds the second largest agricultural land in the world, yet India’s agricultural landscape comprises 85% of small and marginal farmers operating on land holding of 2 acres or less. Thus, lack of economies of scale had made modern technology, resources, experts or farm measurements unaffordable to them. Therefore, much needs to be done by the Government to create suitable market conditions for agritech, taking the return of investment into consideration.

The confluence of IoT, Big Data and Artificial Intelligence has a clear potential to revolutionize farming. What we have seen is just the tip of the agritech iceberg.