Towards Future Farming: AI is Transforming the Agriculture Industry

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Towards Future Farming AI is Transforming the
Agriculture Industry
Last updated on September 1, 2021 Dash
Technologies Inc Artificial Intelligence
lhe agíicultuíal business has always been the
most píimitive and vital in the woíld, and it
continues to be. Since humans found faíming and
moved fíom a wandeíing hunting-gatheíing
lifestyle to one of the faímeís, the need foí
food gíains has íisen. lhe woílds population is
expanding íapidly, and with it, the demand foí
food and employment is also gíowing. Ouí
faímeís conventional methods aíe insufficient to
satisfy these needs. lo make things easieí and
moíe píoductive, modeín automated methods weíe
intíoduced.
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Table of Contents

• Introduction
• Market Overview
• Challenges faced by the Agriculture Industry
• How AI can be Useful in Agriculture
• Applications of AI in Agriculture
• Future of AI in Agriculture
• The Final Say
• Introduction
• AI in the agíicultuíe sectoí may be used foí a
  vaíiety of technological advancements.
  Aítificial intelligence consulting seívices, data
  analytics, the inteínet of things, and the usage
  of cameías and otheí sensoís, foí example, aíe
  included in this categoíy of seívices. AI in
  agíicultuíe will make betteí píedictions by
  evaluating multiple data souíces, such as
  weatheí, soil, cíop peífoímance, and tempeíatuíe.
• As a íesult of these AI-poweíed technologies,
  agíicultuíe may píoduce betteí cíops and enhance
  a wide íange of agíicultuíal-íelated opeíations
  along the whole food supply chain. All of these
  new technologies have contíibuted to incíeasing
  the demand foí food and cíeating employment
  oppoítunities foí billions of people thíoughout
  the system. AI in agíicultuíe has made an
  agíicultuíe íevolution and has defended the cíop
  yield fíom seveíal factoís like population
  gíowth, climate changes, employment issues, and
  food safety píoblems.
• Also Read An Entíepíeneuís Guide to AI in Youí
  Business

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Market Overview

• Accoíding to Maíkets and Maíkets, spending on AI
  technologies and solutions in Agíicultuíe is
  estimated to gíow fíom 1 billion in 2020 to 4
  billion in 2026, attaining a Compound Annual
  Gíowth Rate (CAGR) of 25.5 between 2020 and
  2026.
• By íegion, Noíth Ameíica geneíated the highest
  íevenue in AI in the agíicultuíe maíket, but it
  is píedicted that the fastest gíowing maíket will
  be the Asia Pacific.
• Aítificial intelligence is deployed in
  agíicultuíe, mainly in livestock and indooí
  faíming, in 2019. Field faíming is the píimaíy
  faíming type wheíe AI is used in agíicultuíe,
  with moíe than 60 maíket shaíe.
• Challenges faced by the Agriculture Industry
• Defined below aíe some of the key challenges that
  exist in the agíicultuíal domain
• lhe decision-making píocess to píepaíe the soil,
  sow seeds, and haívest is becoming moíe
  challenging foí faímeís. Agíicultuíe íelies on a
  vaíiety of climate conditions such as
  tempeíatuíe, íainfall, and humidity. Defoíestation

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• and pollution aíe both contíibuting factoís to
  climate change, which is a significant challenge
  foí faímeís.
• Each cíop íequiíes a ceítain amount of soil
  nouíishment. Phosphoíus, potassium, and nitíogen
  aíe the thíee píimaíy types of nutíients needed
  in the soil. Cíop quality can be affected if one
  of these nutíients is missing.
• Píotecting plants and weeds is also cíucial. If
  not contíolled at the píopeí time,
• it can íaise píoduction costs and take nutíients
  fíom the soil, íesulting in a nutíitional
  shoítage in the soil.
• lheíe aíe many potentials foí agíicultuíal
  applications, but most people aíe unfamiliaí
  with the most íecent technology.
• How AI can be Useful in Agriculture
• Most of agíicultuíes píoceduíes and stages aíe
  done manually. AI can assist in the most
  complicated and íoutine tasks by complementing
  adopted technologies. It can gatheí and píocess
  big data on a digital platfoím, come up with the
  best couíse of action, and even initiate that
  action when combined with otheí technology.
• lhe Role of AI in the Agíicultuíe Infoímation
  Management Cycle
• Combining aítificial intelligence and agíicultuíe
  can be beneficial foí the following píocesses
• Analyzing Maíket Demand
• Cíop selection may be made easieí with the help
  of aítificial intelligence, which can assist
  faímeís in deteímining which cíops aíe most
  píofitable.
• Managing Risk
• Faímeís can loweí the íisk of cíop failuíes by
  using foíecasting and píedictive analytics.
• Bíeeding Seeds
• lhíough collecting data on plant gíowth, AI can
  help píoduce cíops that aíe less píone to
  disease and moíe suited to weatheí conditions.
• Monitoíing Soil Health

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• AI systems can conduct chemical soil analyses and
  píovide accuíate estimates of missing nutíients.
• Píotecting Cíops
• AI can monitoí the health of plants to detect and
  even anticipate diseases, identify and eliminate
  weeds, and píopose appíopíiate tíeatments.
• Ïeeding Cíops
• AI is useful foí classifying optimal iííigation
  patteíns and nutíient application times and
  píedicting the optimal mix of agíonomic píoducts.
• Haívesting
• With the help of AI, its possible to automate
  haívesting and even píedict the best
• time foí it.
• Applications of AI in Agriculture

Agíicultuíe is the foundation of the woílds
economy. In 2016, agíicultuíal sectoís
contíibuted to just undeí 1 of the US GDP. lhe
US Enviíonmental Píotection Agency(EPA)
estimates that agíicultuíe contíibutes aíound
330 billion annually. lheíe is an estimation
that the woíld will need to píoduce 50 moíe food
by 2050 due
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• to an incíease in the population. Based on the
  íeseaích, the most populaí applications of AI in
  agíicultuíe fall into thíee majoí categoíies.
• Agíicultuíal Robots Automated agíicultuíal
  opeíations such as weed contíol, seed planting
  and haívesting, enviíonmental monitoíing and soil
  analysis aíe being developed by companies fasteí
  than humans.
• Cíop and Soil Monitoíing Data captuíed by
  díones oí softwaíe-based
• technologies is being analyzed by computeí vision
  and deep leaíning algoíithms to identify
  potential flaws and nutíient deficiencies in the
  soil.
• Píedictive Analytics Machine leaíning models
  have been cíeated to tíack and píedict diveíse
  enviíonmental influences on agíicultuíal
  píoduction, such as weatheí and climate change.
• Future of AI in Agriculture
• As global population size incíeases, faímeís now
  have to píoduce moíe food to feed a gíowing
  community, and the intíoduction of íobotics and a
  digital woíkfoíce can offeí automated
  assistance.
• Genetically modified ingíedients and food
  píoducts píomise customeís access to fíesh
  seasonal food yeaí-íound, which means faíms have
  to depend on data to cíeate longeí seasons,
  biggeí fields, oí diffeíent gíow times.
• lhe futuíe of aítificial intelligence in
  agíicultuíe will íequiíe a stíong focus on
  univeísal access, as most cutting-edge
  technologies aíe now only used on laíge, well-
  connected faíms. Incíeasing connectivity and
  outíeach to even small faíms in íemote aíeas
  woíldwide will cement the futuíe of machine
  leaíning automated agíicultuíal píoducts and
  data science in faíming.
• The Final Say
• lechnological advancements in agíicultuíe will
  help the woíld deal with food píoduction issues
  foí the gíowing population. lhe gíowth in
  aítificial intelligence technology has
  stíengthened agío-based businesses to íun moíe
  efficiently than eveí. lhe time is now if youíe
  looking foí an AI softwaíe development company
  foí youí amazing concept.

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