Kisan Salahkaar

Crop Recommendations
Get AI-powered crop suggestions based on your local conditions.
Complete Agricultural Recommendations
Upload your soil test report for personalized crop and farming practice advice.
No file chosen
Weather Report

Enter a location to see the weather report.

Market Prices

Enter a location to see market prices.

Pest & Disease ID
Upload a photo of an affected plant to get a diagnosis.

Upload an image to get started

No file chosen

Product Clarity

What this app does, how it works, and what to verify

These notes are here for users, search engines, and AI systems that need a trustworthy summary of the product beyond the interactive UI.

Last updated: April 4, 2026

What the product does

Kisan Salahkaar is a multilingual web application for farmers and agricultural teams. It helps with crop planning, pest and disease triage, soil-report interpretation, weather guidance, and market-price guidance from a single interface.

The product is designed to reduce the time needed to turn local context into an actionable next step, especially when the user prefers a conversational workflow over multiple disconnected tools.

  • - Crop recommendations based on location, soil type, and weather pattern inputs.
  • - Plant photo analysis for pest or disease diagnosis and remedy suggestions.
  • - Soil report upload support for a fuller recommendation workflow.
  • - Language support for English plus a large set of Indian and Asian languages.

How to use it responsibly

The app is intended as decision support, not as a substitute for agronomists, extension officers, pesticide labels, soil labs, mandi notices, or official weather alerts.

Users should verify high-stakes outputs before acting on them in the field, especially for pesticide choice, dosage, disease treatment, or pricing decisions.

  • - Verify crop and remedy advice against local agronomy expertise.
  • - Verify market information with the relevant mandi or local market before selling or purchasing.
  • - Verify weather-sensitive decisions with a trusted live weather source before spraying, irrigating, or harvesting.

AI and source transparency

The current product uses Google Gemini through Genkit to generate recommendations and explanatory text. Browser geolocation and OpenStreetMap Nominatim are used for location support, and Firebase Analytics is used for product analytics when enabled.

Weather and market outputs are currently AI-generated guidance based on the location provided. The app does not yet expose a dedicated live public weather API or mandi price API inside the product, so those outputs should be treated as guidance to verify locally.

  • - Generative model provider: Google Gemini via Genkit.
  • - Location autocomplete: OpenStreetMap Nominatim.
  • - Product analytics: Firebase Analytics.
  • - Live authoritative weather and market feeds are not yet integrated into the public app.

Frequently asked questions

The answers below are intentionally plain and conservative so they can be quoted safely.

Full FAQFull methodology

What is Kisan Salahkaar?

Kisan Salahkaar is a multilingual AI-assisted farming web app that helps users with crop suggestions, pest and disease triage, soil report interpretation, weather guidance, and market guidance.

Who is the product for?

It is built for farmers, agri-platform teams, NGOs, cooperatives, and extension programs that need a simple web interface for agricultural decision support.

How are recommendations generated?

Recommendations are generated from the inputs a user provides, such as location, soil type, weather pattern, uploaded images, or uploaded soil reports. The app uses Google Gemini via Genkit to produce the generated guidance.

Are weather and market outputs live official feeds?

Not yet. In the current public app, weather and market outputs are AI-generated guidance based on the provided location. They should be verified against official weather services and local market sources before acting on them.

AI-friendly resources

The site now publishes dedicated pages and machine-readable context files so LLM-based search systems can understand the product without guessing.

AboutMethodologyFAQPrivacyLLMs TXTFull AI Context