[Kurukshetra October Summary] Artificial Intelligence in Agripreneurship (AI in Agriculture) – Explained, pointwise

Introduction

The need to increase agriculture production has become immense as the population is rising at a fast pace. Technology has made significant inroads in the field of agriculture. The growth of start-ups in the field is helping to increase the production and efficiency even for small landholdings. Artificial Intelligence as a field is still developing. There is great potential for the use of AI in agriculture in future especially in reducing the harmful side-effects of agriculture and making it more sustainable. Therefore the need is to strengthen the technology and make it more affordable for the farmers, including for marginal farmers with small landholdings.

Need for AI in Agriculture

Agriculture is one of the oldest human activities. It has been an important factor in the growth of the civilizations. It was a vital factor in permanent settlement of nomadic communities leading to creation of cities and new economies. However, the progress and use of technology in agriculture has been slow. Rising population has put pressure on agriculture production. Increase is population leads to inequitable access to food. Moreover, population is rising in regions where food scarcity is already acute. Finding innovative ways to sustainably improve agricultural productivity, enhance the worldwide food supply chain, reduce food waste, and feed every hungry or malnourished has become a key priority. To make the agriculture and food systems more sustainable, resilient, and inclusive, technological interventions have become imperative. These agricultural improvements allow countries to generate higher yields of better quality food with fewer chemicals. AI in agriculture can help reduce need for physical labour and plug the gap between food demand and supply.

Agriculture has been bedrock of India’s economy. It contributes 18–20% of India’s GDP, 11% of exports and supports ~50% of the workforce. India has the second-largest arable land base and the gross irrigated area. Over 60% of the country’s population, several million small farming households, rely on agriculture as a primary income source. Land remains the important asset for livelihood stability. Agriculture creates jobs and boosts the economy. Agriculture’s growth has boosted rural per capita income. Enhancing growth in Agriculture through deployment of technology will spur rural development, leading to rural transformation.

Applications of AI in Agriculture

NITI Aayog has called Al solutions vital for agriculture. In agriculture, Al technology can be used in decision making e.g., it can tell farmers when to plant, where to use herbicide, and where to expect pest outbreaks.

In recent years, numerous agri-tech businesses have created business models based on AI technologies including machine learning, robotics, and computer vision. Applications of AI, such as alternative credit scoring or “smart” farm equipment, can reduce the cost of serving smallholder farmers across the agriculture ecosystem.

AI in agriculture can help in efficient and sustainable use of resources, and overcome market asymmetries that prevent farmers from accessing regional and global value chains.

Al’s cross-disciplinary uses might revolutionise farming. Al will help farmers do more in less time while improving product quality and crop delivery. Al-supported digital solutions provide companies and entrepreneurs an opportunity to provide smart farms as a service.

AI Technology has also enabled the implementation of precision farming. The utilization of precise amount of inputs at targeted locations at appropriate time reduces the amount of inputs required. It helps in cutting down on waste and save money on labour expenses thus reducing cost of production.

AI technologies are particularly useful in soil management and weed management. Internet of Things (loT), a valuable data processing and storage technology that has extensive use in agriculture as well. The amount of data (organized and unstructured) like weather data, soil reports, rainfall, pest infestations, drone and camera photographs etc. continue to rise rapidly. The data can be analysed by cognitive loT to provide sustainable solutions to agriculture problems e.g., the data can help classify soils of particular field. Then appropriate amount of nutrient input can be suggested by the software and applied precisely through the help of robots.

AI has application in allied-activities like dairy farming as well. AI tools are helping in enhancing the genetics of farm animals. Its most prevalent use is in dairy cow breeding. It has the potential to improve the dairy yields while reducing the susceptibility to diseases.

Challenges to AI in Agriculture

There are significant difficulties associated with the application of Al in agricultural settings/

First, the distribution of modern technology is uneven because of certain geographical, social, or political reasons. This acts as a barrier in adoption of AI in agriculture certain regions.

Second, a lot of improvements have been made over the past few years in the AI systems. However, considerable more work is required to transfer Al-based machines and algorithms from controlled experiments to real agricultural environment. It also requires enhanced ability to handle large sets of data and to interpret them.

Third, there are concerns over the security of devices used to collect the data and the privacy of the data collected.

Agri Tech Startups

At present, India ranks 2nd internationally in ‘agritech’ start-ups. According to the World Economic Forum, India has 3,116 registered food and agriculture start-ups, and this number has grown 25-30% year-over-year. Since 2014, US$ 500 million have been invested in this field.

Breakthroughs in big-data analytics, computer power, and cloud-based storage, together with cost reductions in satellite images, remote sensors, and other technologies, have allowed agri-tech businesses to deploy Al technologies commercially.

DeHaat

DeHaat is an online platform that provides comprehensive agricultural services to farmers. It addresses some of the challenges faced by the farmers through the implementation of Al-enabled solutions. It has helped improve the supply chain efficiency in the agricultural sector. It has brought together buyers, institutional lenders, and agri-input product companies on a single platform. It collaborates with more than 3,000 micro entrepreneurs to provide last-mile delivery and aggregation services. It is operational in the states of Bihar, UP, Odisha, and West Bengal and the network is comprised of more than one million beneficiary farmers from those regions.

See Tree

See Tree was launched in 2017 to provide farmers with vital data for managing and optimising the health of their trees. The firm has created Al systems that track the health of each tree, finding failing trees and groups of healthy trees. It evaluates the impact of various farming approaches and provides actionable data on their effectiveness. It optimises the number of fruits per tree and provides estimates just before harvest. It uses digital farm management to oversee each individual tree and continuously collect data from them. Technology advancements in aerial, ground, and boots-on-the-ground data gathering make it possible to acquire the highest quality information for use in developing the most effective strategies.

Cropln

Cropln is an Agri-Tech Start-up, that provides agribusinesses with decision-making tools that promote consistency, reliability, and sustainability. Cropln is digitising every farm and data-managing the whole ecosystem by providing capabilities for live reporting, analysis, interpretation, and insights that span continents. Their smarter farming solutions are powered in real-time, allowing users to record patterns, forecast trends, and create a business plan for the future. It ensures effective operations, lower expenses, and improved visibility for farmers. It enables companies to profit from actionable information and helps farmers with farm advise and alerts. The predictability of yield quantity and quality, coupled with reduced operational costs, increases business productivity.

Stellapps

Stellapps, is the first company of its kind to concentrate on digitising the dairy supply chain as its primary business objective. Since its founding in 2011, it has been actively promoting the use of technology interventions in the production of milk, especially in developing nations where output per animal is low, traceability is poor, and quality is not up to standards. They have created the SmartMoo platform, which is a full-stack Internet of Things solution, in order to digitalise and optimise milk production, milk procurement and cold chain management. Stellapps’ SmartMoo loT platform receives data from sensors that are installed into milking machines, animal wearables, milk chilling equipment, and milk procurement peripherals. The SmartMoo platform and suite of apps are now responsible for interacting with more than two billion litres of milk each and every year.

Source: Stellapps. Benefits of AI in cattle monitoring, milk procurement and cold chain management.

Conclusion

The use of AI in Agriculture offers exciting opportunities and has the potential to revolutionize the agriculture sector. However, the biggest challenge in adoption of AI in agriculture is small landholdings and lack of access to technology. Another hurdle is scaling up the deployment of AI solutions at mass level (rather than at individual farm levels). The challenges can be addressed by the Agri-tech start-ups with active support from the Union and State Governments. This can help in addressing the challenge of food security as well as making the agriculture sustainable.

Syllabus: GS III, Science and Technology – Developments and their applications and effects in everyday life.

Source: Kurukshetra October 2022