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“If agriculture goes wrong, nothing else will have a chance to go right in the country” ~ M. S. Swaminathan
Agriculture has evolved with mankind through centuries. Today, agriculture contributes 3.8% to the world’s GDP, although the contribution of individual nations across the spectrum varies widely, between 0%-60%. Over the years, while its share in the world economy has reduced vis-à-vis manufacturing and services, the importance of agriculture hasn’t.
The demand for food is never ending and is projected to increase by 70% by 2050 with limited natural resources at disposal. This situation throws up unique challenges; advanced technologies may be a solution. Here's a look at how IBM (IBM) is applying advanced technologies such as Artificial Intelligence (AI) and cognitive computing in the agricultural sector.
The installation of Internet of Things (IoT) devices in farms is increasing and is predicted to be close to 75 million by 2020. With such a huge scale of connected devices and drones, a plethora of structured and unstructured data will be generated which is of significance only if it can be turned into usable, insightful, and applicable information. This is possible via analytics, artificial intelligence and cognitive computing. IBM Watson has been analyzing silos of data and bringing out accurate projections to enable farmers make proactive decisions.
In May this year, India’s National Institution for Transforming India (NITI Aayog) and IBM collaborated to develop a model to provide insights to farmers on improving crop productivity, soil yield, as well as control agricultural inputs. IBM will be introducing climate-aware cognitive farming techniques and identifying systems of crop monitoring, early warning on pest or disease outbreak based on advanced AI innovations across ten districts in its first phase.
Agriculture in India employs 50% of its workforce while contributes a significant 17-18% to the GDP. With around 60% of India’s cropped land dependent on rainfall and a huge segment of population earning their livelihood from agriculture, there is a definite need to bring in mechanisms to enhance agricultural productivity.
This makes having drones and sensors to collect information such as temperature, humidity, precipitation, air pressure, soil pH, light, groundwater and wind essential. It is more important that they are able to utilize the information gathered from the fields. IBM’s capabilities are being used by start-ups such as DroTek to “extract more information and greater precision from tracking devices.” DroTek is enabling farmers to help predict plant strength, suggest irrigation timing among other things that can enhance crop yield.
While testing and chemical analysis of farm soil and water can help maximize crop production, small farmers aren’t able to do so as these processes are expensive, time consuming and labor intensive. IBM’s AgroPad provides a solution. It is a prototype for real-time, on-location, chemical analysis of water and soil using AI built by a team of physicists, engineers and computer scientists in Brazil: “IBM AgroPad is a paper-based water and soil testing strip that uses visual recognition capabilities and machine learning algorithms to determine exact amounts of chemicals in the sample.”
This project solves a crucial problem faced by farmers by providing them with quick, cheap and efficient testing mechanism to decide on the time and quantity of chemicals to be used in farms.
While the above measures focus on use of technology to enhance yield, preservation of good crop from diseases is equally crucial. Globally, an estimated 40% of all potential food production each year is destroyed by insects, plant pathogens and weed pests. One of the most devastating diseases in agriculture is late blight and is traditionally combated by spraying fungicide, even on crops where the disease might not be visibly present.
This has major negative effects.
To reduce the cost and harm done to the environment, IBM has helped scientists to develop a “decision-support system that combines visual and near-infrared image analysis with climate data.” This system is able to predict how likely it is that late blight will strike through images captured are analyzed by Watson Image Recognition via cloud.
The AI in agriculture market is estimated to grow at a CAGR of 22.5% between 2017 and 2025 to touch $2.62 billion by 2025. The adoption of advanced technologies is a way forward to increase crop productivity, reduce use of pesticides and insecticides, better utilize scare natural resources and enhance farm incomes. Agriculture has a new factor of production.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.
