AgricultureEnglish6.5/10 · BronzeCSV file availableUpdated 19 Oct 2026

Artificial intelligence driven crop suitability mapping – Optimizing horticulture for the future | Indian Horticulture - Question and Answer Dataset

Name: Artificial intelligence driven crop suitability mapping – Optimizing horticulture for the future | Indian Horticulture - Question and Answer Dataset
Creator: epubs.icar.org.in
Keywords: Agriculture, question-answering, extractive-qa, artificial-intelligence, crop-suitability-mapping, horticulture, machine-learning, geographical-information-system, remote-sensing, soil-type, climate, topography

#question-answering#extractive-qa#artificial-intelligence#crop-suitability-mapping#horticulture#machine-learning#geographical-information-system#remote-sensing#soil-type#climate#topography

8
Rows: 9.2 KB
File size: CSV
Format: 0
Downloads

Published on DesiDataLicence unspecifiedSigned download links

About this dataset

8 extractive question-and-answer pairs built from Artificial intelligence driven crop suitability mapping – Optimizing horticulture for the future | Indian Horticulture, published by epubs.icar.org.in. Source-evidenced topics include artificial intelligence, crop suitability mapping, horticulture, machine learning, geographical information system, remote sensing, soil type, climate. Every answer is a verbatim span of text the source prints, and each row carries the passage it sits in, its offset in that passage, the source quote, the page and the location in the document, so any row can be checked against the original. Question types in the export: 3 relationship, 2 definition, 2 list, 1 summary. 8 of the 8 pairs (100.0%) ask explanatory questions; the remaining 0 are factual or table-based questions. 100.00% of rows pass the corpus quality gate.

The story in this dataset

AI-driven crop suitability mapping for horticulture in India

This dataset contains eight extractive question-and-answer pairs focused on the role of artificial intelligence in crop suitability mapping, particularly for horticultural crops in India. The data highlights how AI technologies like Machine Learning, Geographical Information Systems, and Remote Sensing are used to analyze environmental factors such as soil type, climate, and terrain. These insights help farmers identify optimal crops for specific geographic areas, improve decision-making accuracy, and promote sustainable agriculture. The dataset is sourced from epubs.icar.org.in and provides verbatim answers from the original publication.

Purpose of crop suitability mapping: Helps farmers identify the best crops for specific geographic areas.

Preview

First 8 of 8 rows

Sample rows from Artificial intelligence driven crop suitability mapping – Optimizing horticulture for the future | Indian Horticulture - Question and Answer Dataset
question	answer	context	answer_start	question_type	knowledge_quality_score	source_document	source_quote	source_location	confidence	validation_status
What does crop suitability mapping help farmers identify?	the best crops for specific geographic areas	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography. For horticultural crops, AI-driven crop suitability mapping offers a transformative way to optimize land use and promote sustainable agriculture. By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers. This data-driven approach improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions.	133	definition	1.000	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography.	p[0]	0.700	valid
Why is AI particularly important for horticultural crops?	highly sensitive to specific soil and climate requirements	The role of AI in crop suitability mapping is particularly important for horticultural crops, which are highly sensitive to specific soil and climate requirements. This article explores the concept of crop suitability mapping, the role of AI in enhancing its precision and efficiency, and how AI is transforming the modern agriculture.	104	relationship	0.970	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	The role of AI in crop suitability mapping is particularly important for horticultural crops, which are highly sensitive to specific soil and climate requirements.	p[0]	0.700	valid
What benefits does AI-driven crop suitability mapping provide to farmers?	improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography. For horticultural crops, AI-driven crop suitability mapping offers a transformative way to optimize land use and promote sustainable agriculture. By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers. This data-driven approach improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions.	723	summary	0.970	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	This data-driven approach improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions.
What is one of the key applications of Artificial Intelligence in agriculture?	crop suitability mapping	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography. For horticultural crops, AI-driven crop suitability mapping offers a transformative way to optimize land use and promote sustainable agriculture. By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers. This data-driven approach improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions.	78	definition	0.940	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography.
What aspects of crop suitability mapping does AI enhance?	precision and efficiency	The role of AI in crop suitability mapping is particularly important for horticultural crops, which are highly sensitive to specific soil and climate requirements. This article explores the concept of crop suitability mapping, the role of AI in enhancing its precision and efficiency, and how AI is transforming the modern agriculture.	259	relationship	0.890	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	This article explores the concept of crop suitability mapping, the role of AI in enhancing its precision and efficiency, and how AI is transforming the modern agriculture.	p[0]	0.700	valid
What does AI-driven crop suitability mapping optimize for horticultural crops?	land use	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography. For horticultural crops, AI-driven crop suitability mapping offers a transformative way to optimize land use and promote sustainable agriculture. By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers. This data-driven approach improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions.	344	relationship	0.890	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	For horticultural crops, AI-driven crop suitability mapping offers a transformative way to optimize land use and promote sustainable agriculture.	p[0]	0.700
What types of environmental data do AI tools process for crop suitability mapping?	soil characteristics, climatic conditions and terrain	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography. For horticultural crops, AI-driven crop suitability mapping offers a transformative way to optimize land use and promote sustainable agriculture. By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers. This data-driven approach improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions.	559	list	0.820	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers.
Which technologies are combined in AI-driven crop suitability mapping?	Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS)	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography. For horticultural crops, AI-driven crop suitability mapping offers a transformative way to optimize land use and promote sustainable agriculture. By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers. This data-driven approach improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions.	403	list	0.820	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers.

Read straight from the file — download or use the API URL for the full dataset.

Use this dataset

Use the API URL with your free DD token in notebooks, scripts, and pipelines.

https://www.desidata.in/api/datasets/epubs-icar-org-in-9866e7fbc229cc64195298293eef3567/download

Dataset downloads are free. For Python or API downloads, sign in once and create a free DD token; set it as DD_TOKEN or save it in your notebook's secrets. Requests are linked to your account so your download history and counts stay accurate.

Open in Colab Download .ipynb

Example workflowsPandasJupyterColabPyTorch exampleHugging Face exampleRAG example

desidata (pip)

# One-time install: pip install desidata
# Set DD_TOKEN in your environment first (create a free token in Profile & settings).
import desidata

df = desidata.load("epubs-icar-org-in-9866e7fbc229cc64195298293eef3567")
df.head()

Preview

First 8 of 8 rows

Sample rows from Artificial intelligence driven crop suitability mapping – Optimizing horticulture for the future | Indian Horticulture - Question and Answer Dataset
question	answer	context	answer_start	question_type	knowledge_quality_score	source_document	source_quote	source_location	confidence	validation_status
What does crop suitability mapping help farmers identify?	the best crops for specific geographic areas	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography. For horticultural crops, AI-driven crop suitability mapping offers a transformative way to optimize land use and promote sustainable agriculture. By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers. This data-driven approach improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions.	133	definition	1.000	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography.	p[0]	0.700	valid
Why is AI particularly important for horticultural crops?	highly sensitive to specific soil and climate requirements	The role of AI in crop suitability mapping is particularly important for horticultural crops, which are highly sensitive to specific soil and climate requirements. This article explores the concept of crop suitability mapping, the role of AI in enhancing its precision and efficiency, and how AI is transforming the modern agriculture.	104	relationship	0.970	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	The role of AI in crop suitability mapping is particularly important for horticultural crops, which are highly sensitive to specific soil and climate requirements.	p[0]	0.700	valid
What benefits does AI-driven crop suitability mapping provide to farmers?	improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography. For horticultural crops, AI-driven crop suitability mapping offers a transformative way to optimize land use and promote sustainable agriculture. By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers. This data-driven approach improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions.	723	summary	0.970	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	This data-driven approach improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions.
What is one of the key applications of Artificial Intelligence in agriculture?	crop suitability mapping	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography. For horticultural crops, AI-driven crop suitability mapping offers a transformative way to optimize land use and promote sustainable agriculture. By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers. This data-driven approach improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions.	78	definition	0.940	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography.
What aspects of crop suitability mapping does AI enhance?	precision and efficiency	The role of AI in crop suitability mapping is particularly important for horticultural crops, which are highly sensitive to specific soil and climate requirements. This article explores the concept of crop suitability mapping, the role of AI in enhancing its precision and efficiency, and how AI is transforming the modern agriculture.	259	relationship	0.890	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	This article explores the concept of crop suitability mapping, the role of AI in enhancing its precision and efficiency, and how AI is transforming the modern agriculture.	p[0]	0.700	valid
What does AI-driven crop suitability mapping optimize for horticultural crops?	land use	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography. For horticultural crops, AI-driven crop suitability mapping offers a transformative way to optimize land use and promote sustainable agriculture. By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers. This data-driven approach improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions.	344	relationship	0.890	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	For horticultural crops, AI-driven crop suitability mapping offers a transformative way to optimize land use and promote sustainable agriculture.	p[0]	0.700
What types of environmental data do AI tools process for crop suitability mapping?	soil characteristics, climatic conditions and terrain	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography. For horticultural crops, AI-driven crop suitability mapping offers a transformative way to optimize land use and promote sustainable agriculture. By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers. This data-driven approach improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions.	559	list	0.820	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers.
Which technologies are combined in AI-driven crop suitability mapping?	Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS)	One of the key applications of Artificial Intelligence (AI) in agriculture is crop suitability mapping, which helps farmers identify the best crops for specific geographic areas by analyzing factors like soil type, climate, and the topography. For horticultural crops, AI-driven crop suitability mapping offers a transformative way to optimize land use and promote sustainable agriculture. By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers. This data-driven approach improves decision-making accuracy, minimizes environmental risks, andaids farmers in adapting to evolving conditions.	403	list	0.820	https://epubs.icar.org.in/index.php/IndHort/article/view/163670	By combining Machine Learning (ML), Geographical Information System (GIS), and Remote Sensing (RS) technologies, AI tools can process complex environmental data such as soil characteristics, climatic conditions and terrain to demarcate the most suitable regions for growing fruits, vegetables, and flowers.

Read straight from the file — download or use the API URL for the full dataset.

Artificial intelligence driven crop suitability mapping – Optimizing horticulture for the future | Indian Horticulture - Question and Answer Dataset

About this dataset

AI-driven crop suitability mapping for horticulture in India

Preview

Use this dataset

Details

Preview