Role of Artificial Intelligence in Agriculture

WHY IN NEWS:

• The Union Agriculture Ministry has selected 100 villages in six states for pilot projects on digital farming, leveraging the use of artificial intelligence (AI) in supporting farmers in multiple activities.

NEED FOR AI IN THE AGRICULTURE SECTOR

• The sector is crucial for overall growth
  • While India has come a long way from being categorised as purely an agrarian economy, agriculture and allied sector still accounts for 49% of India’s workforce, 16% of the country’s gross domestic product, and ensures food security to roughly 1.3 billion people
  • Agriculture and allied sector is critical to India’s growth story.
  • To achieve and maintain an annual growth rate of 8 –10% for the Indian economy, agriculture sector must grow 4% or higher rate.
• The sector is highly dependent on unpredictable variables:
  • Despite making impressive progress and receiving government attention, the sector continues to be dependent on unpredictable variables – monsoon, soil fertility, price of inputs etc.
• Unsustainable practice
  • India has not been able to completely remove its exploitative dependence on resource intensive agricultural practices.
  • Degradation of land, reduction in soil fertility, increased dependence on inorganic fertilizers for higher production, rapidly dropping water tables and emerging pest resistance are some of the several manifestations of India’s unsustainable agricultural practices.
• Threat of climate change:
  • As global climate becomes more vulnerable and unpredictable, dependence on unsustainable and resource intensive agriculture will only heighten the risks of food scarcity and agricultural distress.
• Lower productivity
  • The sector suffers from poor resource utilisation, with the production quantum and productivity still being quite low.
  • For example: yield of cereals, comprising a major share of food grain production, in terms of magnitude is significantly lower than that of China and the USA.
• High price volatility and variable market conditions:
  • When food prices are high, consumers protest and in the years when food prices are low, farmers are in distress and demand loan waivers
• Inefficient water use:
  • Similarly, use of water in agriculture continues to be high and sub-optimal.
  • The practice of growing water intensive crops, and inefficient water management, makes India a net exporter of water and puts India’s long run agronomic sustainability in question
• Abysmal condition of agri-commodity value chain in India:
• 
• Huge agricultural data resource:
  • Due to the diversity of its soil types, climate and topography, India provides a great opportunity for the data scientists and AI experts to develop state of the art AI tools and solutions for agriculture.
  • Indian farms and farmers provide vast and rich data to help create AI solutions for not just the country but the world at large.
  • And this is one of the factors that makes the opportunity for AI in Indian agriculture unparalleled.

STATISTICS

• As per NITI Aayog’s National Strategy for Artificial Intelligence:
  • AI in agriculture to be USD 432 million in 2016 and expects it to grow at the rate of 22.5% CAGR to be valued at USD 2.6 billion by 2025
• According to CB Insights
  • In 2016, approximately 50 Indian agricultural, technology based startups (‘AgTech’) raised USD 313 million
• Examples of Ag-tech startups in India
  • Intello Labs >> uses image-recognition software to monitor crops and predict farm yields.
  • Aibono >> uses agridata science and AI to provide solutions to stabilise crop yields.
  • Trithi Robotics >> uses drone technology to allow farmers to monitor crops in real time and provide precise analysis of their soil.

USE OF AI IN AGRICULTURE

• Increasing the share of price realization to producers:
  • Current low levels of price realisation to farmers (as low as 20% in fruits and vegetables) are primarily due to ineffective price discovery and dissemination mechanisms, supply chain intermediary inefficiency and local regulations.
  • Predictive analytics using AI tools can bring more accurate supply and demand information to farmers >> thus reducing information asymmetry between farmers and intermediaries.
  • As commodity prices are interlinked globally, big data analysis becomes imperative.
  • Data from e-NAM, Agricultural Census, AGMARKET and over 110 million Soil Health Samples provide the volumes required for any predictive modelling
• Soil health monitoring and restoration:
  • Image recognition and deep learning models have enabled distributed soil health monitoring without the need of laboratory testing infrastructure.
  • AI solutions integrated 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.
• Crop health monitoring and providing real time action advisories to farmers:
  • AI can be used to predict advisories for sowing, pest control, input control can help in ensuring increased income and providing stability for the agricultural community.
  • For example, many agronomic factors (such as vegetation health and soil moisture) can be monitored up to the farm level through remote sensing.
  • Using remote sensed data, high resolution weather data, AI technologies, and AI platform, it is possible to monitor crops holistically and provide additional insights to the extension workers/farmers for their farms as and when required.
• AI helps in analyzing farm data:
  • Farms produce hundreds of thousands of data points on the ground daily.
  • With the help of AI, farmers can now analyze a variety of things in real-time such as weather conditions, temperature, water usage or soil conditions collected from their farm to better inform their decisions.
• Helps in seasonal forecasting
  • Farmers are also using AI to create seasonal forecasting models to improve agricultural accuracy and increase productivity.
• Precision agriculture:
  • Precision agriculture uses AI technology to aid in detecting diseases in plants, pests, and poor plant nutrition on farms.
  • NITI Aayog and IBM have partnered to develop a crop yield prediction model using AI to provide real time advisory to farmers.
  • The project is being implemented in 10 Aspirational Districts across the seven states
  • AI sensors can detect and target weeds and then decide which herbicides to apply within the right buffer zone.
  • This helps to prevent over-application of herbicides and excessive toxins that find their way in our food.
• Tackling the labour challenge:
  • With fewer people entering the farming profession, most farms are facing the challenge of a workforce shortage.
  • One solution to help with this shortage of workers is AI agriculture bots.
  • These bots can augment the human labour workforce and are used in various forms.
  • For example: These bots can harvest crops at a higher volume and faster pace than human labourers, more accurately identify and eliminate weeds, and reduce costs for farms by having around the clock labour force.
• Customized and personalized assistance to farmers
  • AI based Chatbots help answer a variety of questions and provides advice and recommendations on specific farm problems.
• Increasing efficiency of farm mechanisation:
  • Image classification tools combined with remote and local sensed data can bring a revolutionary change in utilisation and efficiency of farm machinery, in areas of weed removal, early disease identification, produce harvesting and grading.
  • Horticultural practices require a lot of monitoring at all levels of plant growth and AI tools provide round the clock monitoring of these high value products.

GOVERNMENT INITIATIVES TO PROMOTE AI IN AGRICULTURE

• Bilateral cooperation:
  • India and UK
    • ‘Transforming India’s Green Revolution and Empowerment for Sustainable Food Supplies”, a program in collaboration between U.K. and India, aims to finance interdisciplinary research in AI in agriculture
• Cooperation with private sector:
  • Union Agriculture Ministry has signed a MoU with Microsoft
    • It aims to promote digital agriculture in selected 100 villages of India.
  • Government of India has signed an MOU with IBM
    • To use artificial intelligence (AI) to secure the farming capabilities of Indian farmers.
    • It will provide weather forecast and soil moisture information to farmers to take pre-informed decisions regarding better management of water, soil and crop.
• Introduction of Soil Health Card Scheme
  • Soil health card provides information to the farmers on nutrient status of their soil along with recommendations on appropriate dosage of nutrients to be applied for improving crop productivity and soil fertility.
• Kisan Suvidha and PusaKrishi mobile applications
  • It aims to facilitate dissemination of information to farmers on the critical parameters viz., weather, market prices, plant protection, soil health card, cold storages
  • Thus, they can make informed decisions to sell produce at the right price and right time.
• E-NAM
  • Launching of e-National Agriculture Market initiative to provide farmers an electronic online trading platform.
• M-Kisan Portal
  • Development of mKisan Portal for sending advisories on various crop related matter to the registered farmers through SMSs.
• AGRI-UDAAN Food and Agribusiness Accelerator 2.0 programme
  • It is an attempt to promote innovation and entrepreneurship in agriculture.
  • It will mentor startups and help them connect with potential investors.
• Efforts to disseminate technologies among farm community:
  • Government has set up Krishi Vigyan Kendras and Agricultural Technology Management Agencies at district level for dissemination of technologies among farm community
• Assisting farmers in accessing agri-extension services
  • ‘Farm Machinery package for Different Agro-Climatic Zones in India’ mobile application
    • It gives information on farm machinery package available for state-wise, agro-climatic zone wise, district-wise, cropping pattern wise and power source wise.
  • ‘My Ciphet’ mobile application
    • To help farmers to get precise information regarding the ICAR developed post-harvest technologies, products and machineries.
  • Efforts from ICAR:
    • ICAR has also compiled more than 100 mobile apps developed by ICAR, State Agricultural Universities and Krishi Vigyan Kendras and uploaded on its website.
• Sub-Mission on Agricultural Mechanization (SMAM) scheme:
  • Under it, subsidy is provided for purchase of various types of agricultural equipment and machinery
  • It aims to promote ‘Custom Hiring Centres’ and ‘Hi-tech Hubs of High-Value Machines’ to offset the adverse economies of scale arising due to small and fragmented landholding and high cost of individual ownership.
• RAISE (Responsible AI for Social Empowerment) 2020
  • It is a first of its kind, global meeting of minds on Artificial Intelligence to drive India's vision and roadmap for social transformation, inclusion and empowerment through responsible AI.
  • This includes using AI in areas like Agriculture, along with other sectors
• State-wise initiatives:
  • For ex: Maha Agri Tech Project in Maharashtra
    • It seeks to use innovative technologies to address various risks related to cultivation such as poor rains, pest attacks, etc., and to accurately predict crop yielding.
• Use of AI in implementation of farmers welfare programmes
  • Pradhan Mantri Fasal Bima Yojana (PMFBY)
    • This is a government-sponsored crop insurance scheme that integrates multiple stakeholders on a single platform.
    • Under this scheme, the government uses technologies like AI, remote sensing imageries to reduce the time lag for settling of claims of the farmers.
  • PM-KISAN
    • Under the scheme, the Centre transfers Rs 6,000 per year directly into the bank accounts of the all landholding farmers
    • The government is aimed to leverage the huge amount of collected data by several agri-schemes and use the same to better target the farmer who requires the benefit of PM-KISAN.

CHALLENGES IN ADOPTION OF AI:

• Unequal access:
  • Rich farmers are adopting the technology and utilizing their services but the small and marginal farmers are unable to afford the new technologies and they remain left out.
• Poor data security:
  • Without enabling data security legal framework, enormous data collected by emerging technologies can be misused by monopolies and transfer out of country
• Lack of infrastructure:
  • Internet connectivity, undisrupted electricity are must for exploiting these technologies >> In rural areas, such infrastructure is not reliable
• Lack of know-how:
  • In rural areas, insufficient connectivity, along with lack of basic computer knowledge, high costs for services and illiteracy hinder rapid development of electronic-agriculture.
• Human resource shortage:
  • Skilled manpower to provide extension services in these technologies is lacking
• Poor land record management and land fragmentation
  • It prevents use of these emerging technologies due to cost benefit considerations

WAY FORWARD

• Ensuring participation of both public and private sector.
  • With the recent reforms in the Agriculture sector, there is a likelihood of increased investments in contract farming and infusion of technology for better yields and productivity >> This will further push the adoption of AI in agriculture.
• Outreach activities:
  • There is a need to tap the vast network of Panchayats and local government to undertake awareness and outreach activities to ensure adoption of AI by farming community.
• Digital literacy:
  • Spreading digital literacy, by teaching farmers how to choose and use apps, which are, or soon to be, available in regional languages.
• Need for investment and knowledge exchange.
  • Private sector agro-start-ups in India that use AI to tackle soil monitoring, crop health monitoring etc. is in nascent stage and they >> require further investment and knowledge exchange.
• Need for a robust data protection regime:
  • Collection of information about landholdings and farming practice for application of AI >> may be a potential avenue for invasion of privacy >> thus we need to enact Personal Data Protection Act as recommended by B N Srikrishna committee

BEST PRACTICE

• Plantix - AI based application for soil care
  • PEAT, a Berlin-based agri-tech startup has developed a deep learning application called Plantix that reportedly identifies potential defects and nutrient deficiencies in the soil.
  • The image recognition app identifies possible defects through images captured by the user’s smartphone camera
• AI Sowing App
  • Microsoft in collaboration with ICRISAT, developed an AI Sowing App
  • The app sends sowing advisories to participating farmers on the optimal date to sow

PRACTICE QUESTION:

Q. Write a brief note on application of AI based technology in tackling agrarian distress in India?