benefits of AI in Agriculture Archives

Last Update: March 2025 (Artificial Intelligence)

Artificial Intelligence

This article deals with ‘Artificial Intelligence – UPSC.’ This is part of our series on ‘Science and Technology’ which is an important pillar of the GS-3 syllabus. For more articles on Science and technology, you can click here

Introduction

Artificial Intelligence (AI) is a branch of computer science focused on enabling machines to perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and decision-making.
It is concerned with
- Learning from Experience
- Recognising images (e.g., facial recognition in smartphones)
- Solve Complex Problems
- Understand Human Language
- Create perspectives
The Nobel Prize (2024) for Physics was awarded to John Hopfield and Geoffrey Hinton, regarded as fathers of Artificial Intelligence, for training in Artificial Neural Networks (ANNs).

Side Topic: Traditional AI vs Generative AI

	Traditional AI	Generative AI
Key Focus	Analyses data, performs specific tasks and automate decision making	Creates new data (text, images, music etc.)
Learning Approach	Explicit rules and algorithms	Data driven learning (Neural Networks)
Output	Structured outputs such as predictions, solutions or classifications	Entirely new content or creative outputs
Adaptability	Require manual intervention and reprogramming	Automatically adjust and improve its performance over time.

Benefits and Uses of AI

The benefits and uses of Artificial intelligence are immense and can be categorised in the following way

Governance

Policing: AI can be used to analyse crime patterns to allocate resources (e.g., Geolitica (formerly PredPol) is a company involved in predictive policing).
Smart Traffic Management: Cities like Bengaluru use AI to optimise signal timings.
Analyse Government Scheme: AI can be used to track the real-time performance of schemes like PM-KISAN.

Agriculture

Precision Farming: AI is used in Precision farming which is driven by data from soil analysis and crop monitoring. For example, Microsoft’s FarmBeats combines data with farmers’ knowledge and intuition to increase farm productivity and reduce costs.
Predictive Analytics: AI can be used in weather forecasting, pest control, etc., and plays an essential part in farming. For example, the Plantix app can identify pest infestations using an image of the plant.

Financial Sector

Fraud Detection: AI can flag suspicious transactions (e.g., Mastercard’s Decision Intelligence).
Robo-Advisors: Platforms like Zerodha’s Coin offer AI-driven investment advice.

Healthcare

Early Disease Detection: AI can be used for Early Disease Detection using processes like Google’s AI for Diabetic Retinopathy (prepared by Google by working to team of Ophthalmologists to identify signs of diabetes from eye scans).
Robotic Surgery: AI can help in performing robotic surgery. For example, the Da Vinci Surgical System translates a surgeon’s hand movements into real-time at the console.
Apple Watch uses AI to monitor heart rhythms and alert users about heart attacks.

Education

Personalised Learning: AI can enable personalised learning, create smart content, and automate grading and assessments. For example, Platforms like BYJU and Unacademy use AI for adaptive learning.
Proctoring: AI monitors online exams for malpractice (e.g., Proctorio).

Manufacturing

Predict Equipment Failure: AI can predict equipment failure using tools like GE Predix and help reduce downtime.

Logistics

Better Logistics: Uber and Google Maps suggest the best route using AI.
Warehouse Automation: Robots like Amazon’s Kiva sort and transport goods efficiently.
Driverless Cars: Autonomous vehicles that use AI for navigation

Energy

Smart Grids: Smart Grids balance the supply and demand of energy using AI and help integrate renewable sources (like Solar and Wind) into the grid.
Optimise Energy Use: Systems like Google’s Nest optimise HVAC usage, cutting energy costs by 20–30%.

Other Uses

Generative AI: These models generate human-like text, answer questions, and perform conversational tasks. Examples include ChatGPT, Deep Seek, etc.
Personal Assistants: Companies like Google, Amazon(Alexa), etc., have developed personal assistants. They work on AI. Hence, now everybody can have personal assistant for free.
AI-Powered Prosthetics: Devices like Open Bionics’ Hero Arm adapt to user movements.
Games playing: AI-intelligent games learn from their mistakes and are not monotonous.

Challenges and Ethical Concerns

Ethical-Moral Impulses: AI lacks human qualities like compassion, raising concerns about decision-making in critical situations.
Bias and Prejudice: Since AI Models are trained from existing literature and data, they can inherit human biases, leading to unfair outcomes.
Plagiarism: AI can be used for creating fake content and manipulating public opinion such as fake videos of politicians.
Job Loss: AI threatens jobs in IT, transportation (e.g., self-driving cars), and even professions like law and medicine.
Exacerbate Inequality: AI may exacerbate inequality by favouring high-skilled jobs over low-skilled ones.
Lack of Accountability: There is no clear distinction of who is to be held accountable, in case there was an unfavourable outcome
Super-intelligence: A sufficiently intelligent AI system can redesign itself or create a better successor system, leading to an intelligence explosion.
Environmental Impact: High energy consumption during AI model training contributes to carbon emissions.
AI Colonialism: Dominance of AI development by a few countries (e.g., the U.S., China, and Europe) creates a digital divide, reinforcing existing inequalities in economic, social, and political systems.
Threat to Democracy: AI can be used to manipulate elections, spread fake news, and influence public behaviour.

Why India should invest in building own AI Models?

Data Sovereignty & Security: AI models require vast amounts of data for training, and relying on foreign models can lead to data security risks.
Cultural & Linguistic Relevance: Most global AI models are trained on datasets that do not fully represent India’s diverse linguistic and cultural landscape. Indigenous AI models can be designed to better understand regional languages, dialects, and cultural contexts, leading to more accurate and inclusive AI solutions.
Economic Growth & Job Creation: Investing in AI model development can boost India’s tech ecosystem, creating new opportunities for AI researchers, engineers, and startups.
Cost Efficiency & Independence: Relying on foreign AI models often involves licensing fees and restrictions. Developing indigenous AI models can reduce costs in the long run.

Indian Government and Artificial Intelligence

1. IndiaAI Mission (2024)

The mission was approved by the Union Cabinet in 2024. It aims to establish a comprehensive AI ecosystem in India. It is to be achieved by

Democratizing Computing Access: Setting up affordable High-Performance Computing (HPC) infrastructure for startups, researchers, and academia.
StartUp Risk Capital: Financial support for early-stage AI ventures.
Socially Impactful AI: Focus on healthcare, agriculture, education, and sustainability.
Ethical AI: Ensuring transparency, accountability, and fairness.

Key components of the mission include

India AI Compute Capacity: Build 10,000+ GPU-based supercomputing infrastructure.
India AI Innovation Centre: Develop indigenous large multimodal models (LMMs) tailored to Indian languages and contexts.
India AI Datasets Platform: Host non-personal datasets (e.g., agriculture, healthcare) for public and private use.
IndiaAI Application Development Initiative: Promote AI adoption in governance (e.g., predictive policing, smart cities).
IndiaAI Startup Financing: Fund 500+ AI startups through grants, loans, and VC partnerships.
IndiaAI FutureSkills: Upskill 1 million professionals via online courses
Safe & Trusted AI: Ensuring ethical AI governance is aligned with the Digital Personal Data Protection Act (2023).

2. BharatGen Program

Initiative of the Ministry of Science and Technology.
It is a Large Language Model (LLM) project focused on developing a generative AI system capable of generating high-quality text, audio and images in various Indian languages.

3. Bharat Data Sagar

It aims to establish a vast repository of India-centric data that ensures that the AI models are deeply rooted in the country’s unique context.

4. National Strategy for Artificial Intelligence

NITI Aayog has identified five areas where AI can be useful. These include

Healthcare
Agriculture
Education
Smart cities and infrastructure
Transportation

5. Digital Personal Data Protection Act

It aims to strengthen data privacy and address AI-related concerns.

6. Budgetary Support

2022 Budget: AI declared a sunrise sector with tax incentives for startups.
2018 Budget: ₹100 crore allocated for National Mission on Cyber-Physical Systems

Global Measures

AI Action Summit: Summit was held in France to tackle potential risks of AI.
Hiroshima AI Process: It is an effort of G7 countries to regulate AI.
Global Partnership on Artificial Intelligence (GPAI): To guide the responsible development and use of AI. India is a founding member.
EU Framework for AI: It has divided the AI applications into four risk classes and
- Prohibited Applications:
  - Mass-scale facial recognition systems (with limited exemptions for law enforcement)
  - AI systems aimed at behavioural control or manipulation
- High-Risk Applications:
  - Examples include AI tools for self-driving cars.
  - These applications are permitted but require certification and must make their backend techniques open to public scrutiny.
- Medium-Risk Applications:
  - Includes technologies like generative AI chatbots.
  - Allowed without restrictions but must provide detailed documentation of their functioning.
  - Users must be clearly informed that they are interacting with an AI, not a human.
- Low-Risk Applications:
  - Minimal regulations, focusing on transparency and user awareness.