Lok Sabha Question No. 1576 (UNSTARRED), answered on 2025-12-09
Subject: Use of AI in Agriculture Supply Chain
Ministry: AGRICULTURE AND FARMERS WELFARE
Asked by: Shri Kesineni Sivanath

## GOVERNMENT OF INDIA MINISTRY OF AGRICULTURE AND FARMERS WELFARE DEPARTMENT OF AGRICULTURE AND FARMERS WELFARE

## LOK SABHA UNSTARRED QUESTION NO. 1576

TO BE ANSWERED ON THE 09 TH  DECEMBER, 2025

## USE OF AI IN AGRICULTURE SUPPLY CHAIN

## 1576. SHRI KESINENI SIVANATH

Will the Minister of AGRICULTURE AND FARMERS WELFARE क ृ िष एवं िकसान कʞाण मंũी be pleased to state:

- (a) whether the Government has implemented pilot projects using Artificial Intelligence (Al) in the agriculture supply chain, including applications such as blockchain-based traceability, warehouse management, quality assessment, grading, forecasting, logistics optimisation and procurement monitoring;
- (b) if  so,  the  details  of  such  pilot  projects,  including  implementing  ministries/agencies, locations, commodities covered, technological solutions deployed and key outcomes;
- (c) the  details  of  collaborations  made  with  private  sector  partners,  agri-tech  start-ups, research institutions and technology companies for developing, testing or scaling Al-enabled agriculture supply chain technologies;
- (d) whether any assessment has been carried out on the impact of these Al solutions on efficiency,  transparency,  reduction  of  post-harvest  losses,  farmer  incomes  and  market linkages, and if so, the details thereof; and
- (e) the  measures  taken  by  the  Government  to  replicate  or  scale  successful  AI-based agriculture supply chain models across additional States, warehouses, mandis and FPOs?

## ANSWER

MINISTER OF STATE FOR AGRICULTURE AND FARMERS WELFARE क ृ िष एवं िकसान कʞाण राǛ मंũी (SHRI RAMNATH THAKUR)

- (a) to (e): The government has employed Artificial Intelligence (AI) methods to improve crop productivity, sustainability and farmer livelihoods and to address various challenges in the agricultural sector. Some initiatives are given below:
- I.  'Kisan e-Mitra' is a voice-based AI-powered chatbot, developed to assist farmers with responses to their queries on PM Kisan Samman Nidhi scheme, PM Fasal Bhima Yojna and Kisan Credit Card. This solution supports 11 regional languages and is evolving to assist with other government programs. At present, it handles over 8000 farmer queries daily and so far, more than 93 lakh queries have been answered.
- II.  The National Pest Surveillance System, for tackling the loss of produce due to climate change, utilizes AI and Machine Learning to detect pest infestation in crop issues, enabling timely intervention for healthier crops. This tool, currently used by over 10,000 extension workers, allows farmers to capture images of pests to help them mitigate pest attacks and reduce crop losses. At present, it supports 66  crops  and  over  432  pests. AI-based  analytics  using  field  photographs  for satellite-based crop mapping is being used in Crop-weather matching monitoring of crops sown.
- III.  Further, an AI-based pilot was conducted in collaboration with the Development Innovation Lab- India on agriculturally relevant local monsoon  onset forecasts across parts of 13 states in India for Kharif 2025. An open-source blended model was  used,  including  NeuralGCM,  the  European  Centre  for  Medium-Range Weather Forecasts' (ECMWF) Artificial Intelligence Forecasting System (AIFS), and  historical  rainfall  data  from  125  years  from  the  India  Meteorological Department (IMD). The probabilistic forecasts predicted only the local onset of the monsoon, which is essential for deciding on the date of sowing crops. Local monsoon  onset  forecasts  were  sent  via  SMS  through  the  M-Kisan  portal  to 3,88,45,214 farmers in 13   states in five regional languages- Hindi, Odia, Marathi, Bangla,  and  Punjabi.  Telephonic  farmer  feedback  surveys  were  conducted  in Madhya Pradesh and Bihar through Kisan Call Centres after the forecasts were sent. The survey revealed that 31-52% farmers adjusted their planting decisions, primarily through changes in land preparation and sowing timing, which included crop and input choice.

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