Smart Agriculture: Digital Agriculture

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  • Green revolution in the second half of the 20th century.
  • Need of technology revolution.
  • Data driven, intelligent, agile, autonomously connected system of systems.
  • Potential to double the food production in 40 years.
  • Lesser impact on climate agriculture.
  • Can reduce the losses and wastage by 50 % .
  • At present irrigation water-use accounts for 80 % of the available water.
  • The global water withdrawal grew 1.7 times faster than the population according to FAO estimates.
  • According to an estimate the irrigation requirement has to be lowered to the level of 68 % of the total demand by 2050.
  • To increase global food supplies on a sustainable basis:
    • Improving agricultural productivity.
    • Conserving and enhancing natural resources like water.
  • The Economic Survey 2018 - 19:
    • Focus should shift from land productivity to irrigation water productivity.
    • Thrust should be on micro-irrigation that can improve water use efficiency.
  • National Mission on Micro Irrigation (NMMI) Study:
    • Conducted during 2014.
    • Covers 64 districts in 13 states.
    • Micro-irrigation has benefitted the farmers.
    • The electricity consumption has been reduced by about 31 % .
    • The irrigation cost has also decreased by an avg. of 32.3 % .
    • 28 % reduction total fertilizer consumption.

Digital Agriculture

  • Digitizes the planning, process and result of agricultural production:
  • Big Data, AI, Cloud Computing and Block chain.

Precision Agriculture

  • Uses information technology.
  • To achieve precision management such as drone, robot and intelligent irrigation.

Transformative Discoveries for Smart Agriculture

Artificial Intelligence

  • Science of instilling intelligence in machines.
  • Capable of doing tasks that traditionally required human minds.
  • AI is commonly used when a machine mimics cognitive function:
    • Planning
    • Learning
    • Reasoning
    • Problem solving
    • Knowledge representation
    • Perception
    • Motion
    • Manipulation
    • Social intelligence and creativity
  • AI combines automation, robotics and computer vision.
  • Advances in AI have been augmented with the advancement in statistics, faster computers with access to large amounts of data.
  • Integration of AI and IoT:
    • Improves the growing and selling processes by predictive analytics.
    • Helps farmers in determining which crops to grow.
    • Anticipate potential threats
    • Combining historical information about weather patterns
    • Crop performance with real time data.

Block Chain

  • A recent technological advancement.
  • Potential for addressing the challenges of creating a more transparent, authentic and trustworthy digital record.
  • Works by mapping data and providing it to the users along the value chain supply by scanning a barcode.
  • Barcodes are linked and applied throughout the value chain.
  • Grading and sorting robotics automatically.
  • A cost effective supply chain with transparency to optimize profits.
  • Block chain integrated with IoT:
    • Creates an immutable supply chain.
    • Ensures buyers are getting an authentic product.
    • The products have not been damaged along the way.
    • Also verifies whether a product containing hazardous materials has been disposed of correctly and safely.

Robotics

  • Powered with advanced AI technology.
  • The real situation on the condition of crops on ground can be assessed using drones with AI enabled vision processing capabilities.
  • Autonomous drones and data provided:
    • Helps in crop monitoring
    • Soil assessment
    • Plant emergence and population
    • Fertility
    • Crop protection and crop insurance
    • Reporting in real time
    • Irrigation and drainage planning and harvest planning

Autonomous Swarms

  • Combine the technology of swarm robotics with a block chain-based backend.
  • Involves multiple copies of the same robot
  • Working independently in parallel.
  • To achieve a goal too large for ant robot to accomplish.
  • Pesticides and fertilizer can be applied more sparingly.
  • Produces greater yields at reduced cost.
  • Raises the crop quality.

Artificial Intelligence of Things (AIoT)

  • Internet of Things (IoT)
  • Artificial Intelligence (AI)
  • Powerful technologies
  • A set of tasks or learn from data can be completed by AI in an intelligent way.
  • Devices empowered with combination of AI and IoT:
    • Can Analyse data and make decisions.
    • Act on the data without involvement on humans.

Big Data

  • A combination of technology and analytics.
  • Can collect and compile novel data.
  • Processing in a more useful and timely way to assist decision making.
  • Big Data and analytics have the potential to add values across each step.
  • Also Big Data and analytics can streamline food processing value chains such as selection of right agri-inputs, monitoring soil moisture, tracking prices of market, controlling irrigations, finding the right selling point and getting the right price.
  • Data Mining:
  • Computing process of discovering patterns.
  • Large data sets involving methods at the intersection of:
    • Artificial intelligence
    • Machine learning statistics
    • Database system

Focus on Higher Water-Use Efficiency

  • Northwest India had the highest groundwater depletion rates in 2002 - 2008 in the world as per NASA՚s Gravity Recovery and Climate Experiment Data.
  • India records only 38 % water use efficiency in agriculture.
  • There are perhaps 20 million groundwater irrigators and 14 million tube wells in India.
  • Efficiency is defined (In Irrigation Systems) :
    • Amount of water used by the plant divided by the total amount of water applied to the field.
  • Crop per Drop:
    • Idea of growing more food with the same amount of water or less.
    • Generally, increases the productivity of water.
  • Three major irrigation systems:
    • Gravity irrigation systems are the least efficient.
    • Sprinkler systems more efficient.
    • Micro-irrigation is the most efficient.
  • Compared to pressurized systems unpressurized systems are generally less efficient.
  • Drones equipped with hyper spectral, multispectral, or thermal sensors are able to identify areas that require changes in irrigation.
  • By measuring the crop՚s heat signature, sensors are able to calculate the vegetation index and indicator of health through AI.
  • Analog irrigation systems:
    • Used in commercial agriculture for some time.
    • Operate on pre-programmed schedules and timers.

Need for Water Accounting

  • Includes sophisticated approaches to demand forecasting.
  • Basis of demographic change, urbanization, industrialization and energy production.
  • An essential underpinning to transparent and effective water allocation systems.
  • Developed in countries with varying levels of sophistication and effectiveness:
    • Australia
    • China
    • France
    • Iran
    • US
  • Accounting needs to be accompanied by regular assessments of governance, institutions, public and private expenditure, legislation and the wider political economy of water.
  • Malaysia a good example:
  • Invested in improving national water accounting and auditing processes.
  • The country experiences seasonal water stress despite having abundant water resources.
  • National Water Balanced System has been established.
  • NWBS is being implemented using hydrological and other computer models in the granary regions of the country.

Smart Irrigation Technologies Fall under Two Classifications

Sensor-Based Control

  • Leverages real-time measurements.
  • From locally installed sensors.
  • To automatically adjust irrigation timing
  • To the exact temperature, rainfall, humidity and soil moisture.
  • To ensure farmers are able to anticipate unfavourable conditions the data is also supplemented with historic weather information.

Signal-Based Control

  • Rely on weather updates.
  • Transmitted by radio, telephone and web-based applications.
  • The signals are sent from local weather stations to update the “evapotranspiration rate” of the irrigation controller.

Initiatives of Smart Agriculture

  • National Strategy for Artificial Intelligence in India:
    • NITI Aayog.
    • Focus on economic growth and social inclusion.
  • Can double the food production with lesser impact on climate change.
  • IoT has the potential to increase the agricultural productivity by 70 % by 2050.
  • To secure the farming capabilities of the farmers, the GoI signed an MOU with IBM to use AI.
    • Pilot study to be conducted in Madhya Pradesh, Gujarat and Maharashtra.
    • For improving the agriculture sector IBM՚s Watson Decision Platform will provide a farm level solution after the pilot study.
    • Provide weather forecast and soil moisture information to farmers.
    • Farmers can take pre-informed decisions regarding better management of water, soil and crops.

AGRI-UDAAN

  • To push innovative technologies in the agriculture sector.
  • To mentor 40 agricultural start-ups.
  • Cities like Chandigarh, Ahmedabad, Pune, Bengaluru, Kolkata and Hyderabad.
  • Enabling them to connect with potential investors.
  • Maha Agri Tech Project:
    • Maharashtra
    • Seeks to use innovative technologies.
    • To address various risks related to cultivation such as poor rains, pest attacks, etc.
    • To accurately predict crop yielding.
    • First phase:
      • Use of satellite images.
      • Data analysis is being done by Maharashtra Remote Sensing Application Centre (MRSAC) along with National Remote Sensing Centre (NRSC) .
    • Second phase:
      • Analysis of the data collected.
      • To build a seamless framework for agriculture modelling
      • A geospatial database of soil nutrients, rainfall and moisture stress.

Needs to be Done

  • To develop an infrastructure in our agricultural institutions so as to have scientific understanding for such technologies.
  • Farmers need to be trained to use latest technologies and equipments in the field.
  • A need for convergence of available institutional resources in the country.
  • To rope in with world class institutions like IIT, NIT, IISc etc. along with top agriculture institutions Indian Agricultural Research Institute, Indian Veterinary Research Institute, National Dairy Research Institute, Indian Institute of Horticultural Research for testing and validation of the suitable technologies in commercially important crops.
  • Second PM of Australia Alfred Deakin in 1990 said:
    • “It is not the quantity of water applied to a crop; it is the quantity of intelligence applied which determines the result - there is more due to intelligence than water in every case.”

Agribot: Saving Water and Spraying Pesticides

  • Trials are being conducted in the fields with Agribot drones.
  • Spraying of pesticides with limited amount of water is one of the great features of the Agribot drone.
  • Upto 400 litres of water is used for spraying pesticides in one acre field, the Agribot can spray it in 8 litres of water.
  • Pesticides are sprayed about 10 times a year per acre.
  • Around 392 litres of water is saved per acre and in 1 year it comes to be 3920 liters.
  • There are about 39 crore acres of cultivated land in India.
  • About 1.5 lakh crore litres of water can be saved if pesticide spraying is made mandatory by drone.
  • Agribot drones are also being used to control grasshoppers.
  • In January 2020, the drone sprayed over 500 hectares of land in 16 days and freed the area from locusts.
  • It takes about three minutes for a drone to spray on one hectare of land.
  • Around 99 % of grasshoppers pile up in about 10 minutes.
  • Preparations are being made to eliminate the locusts by drones.
  • They are also able to operate in inaccessible areas and mountains.
  • With additional batteries, the Agribot drone can cover 50 acres a day.
  • By spraying pesticides with drones, farmers stay away from chemicals and they do not have any side effects on their health.

Blue Tentacles

  • An Italian start-up.
  • A “precision-based” AI system that takes note of humidity, temperature, climate data and forecasts as well as satellite data.
  • To help farmers improve their irrigation practices whilst preventing water wastage and conserving energy.
  • These digital technologies are already being used by a number of large scale companies.

An IoT Based Autonomous Irrigation Solution

  • Developed by Vijay Bhaskar Reddy, a software engineer in Andhra Pradesh.
  • Mobile Motor Controller Device- Kisan Raja which helps farmers monitor, control and utilise water judiciously.
  • This device has helped more than 34,200 farmers across ten states namely Telangana, Andhra Pradesh, Karnataka, Maharashtra, Tamil Nadu, Haryana, Punjab, Rajasthan, Madhya Pradesh and West Bengal.
  • The smart irrigation market was valued at USD 0.83 billion in 2018 and is expected to reach USD 1.76 billion by 2023, at a CAGR of 16.30 % according to According to research company Markets.

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