10 extractive question-and-answer pairs built from Artificial intelligence-assisted technologies in horticultural crop management, published by epubs.icar.org.in. Source-evidenced topics include artificial intelligence-assisted technologies, horticultural crop management, nutritional security, yield improvement, pathogen detection, resilience, high-throughput sensors, satellite imagery. 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: 7 list, 2 summary, 1 factual. 9 of the 10 pairs (90.0%) ask explanatory questions; the remaining 1 are factual or table-based questions. 100.00% of rows pass the corpus quality gate.
This dataset contains 10 extractive question-and-answer pairs focused on artificial intelligence-assisted technologies in horticultural crop management, sourced from epubs.icar.org.in. The questions explore topics such as AI's role in optimizing productivity, diagnosing diseases, and promoting sustainable practices. The answers are verbatim excerpts from the source, providing insights into how AI analyzes data from high-throughput sensors, satellite imagery, and climate models to improve crop resilience and yield. The dataset also highlights the benefits of horticultural crops for human well-being and the advanced tools used for image classification in crop monitoring.
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# One-time install: pip install desidata
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import desidata
df = desidata.load("epubs-icar-org-in-22a1c7257e3d7a15e25a510420de252f")
df.head()First 10 of 10 rows
| question | answer | context | answer_start | question_type | knowledge_quality_score | source_document | source_quote | source_page | source_location | confidence | validation_status |
|---|---|---|---|---|---|---|---|---|---|---|---|
| What outcomes can deep learning technologies provide for horticultural crop management? | reliable predictive outcomes for complex and uncertain phenomena. | Horticultural crops are essential for human well-being, as they provide crucial nutrients that support a balanced diet, enrich cultural landscapes, and contribute to nutritional security. To meet the demands of growing population, it is imperative to enhance both the quantity and quality of horticultural produce while considering limited stakeholder values. Integrating artificial intelligence (AI) into horticultural crop management is essential for optimizing productivity, diagnosing diseases, and enhancing value-addition. AI can help understand domestication events, adaptation processes, yield improvement, pathogen detection, and resilience by rapidly analyzing vast amounts of data from high-throughput sensors, satellite imagery, and climate models. By assimilating diverse data sets, deep learning technologies offer reliable predictive outcomes for complex and uncertain phenomena. | 829 | summary | 0.970 | https://epubs.icar.org.in/index.php/IndHort/article/view/163681 | By assimilating diverse data sets, deep learning technologies offer reliable predictive outcomes for complex and uncertain phenomena. | p[0] | 0.700 | valid | |
| What practices do advanced technologies promote in the horticulture sector? | resource-efficient and sustainable practices | The applicability of advanced tools and technologies for significant improvements in horticultural crops and their monitoring through sustainable practices is discussed. These technologies facilitate effective classification and recognition of images of flowers, fruits, and vegetables, as well as phenotypicidentification based on various attributes, enhancing productivity and economic returns. they leverage the horticulture industry through automated precision farming, predictive analytics, and supply chain optimization; ultimately promoting resource-efficient and sustainable practices to advance the Horticulture Sector. | 548 | summary | 0.890 | https://epubs.icar.org.in/index.php/IndHort/article/view/163681 | they leverage the horticulture industry through automated precision farming, predictive analytics, and supply chain optimization; ultimately promoting resource-efficient and sustainable practices to advance the Horticulture Sector. | p[0] | 0.700 | valid | |
| What are the benefits of horticultural crops for human well-being? | crucial nutrients that support a balanced diet, enrich cultural landscapes, and contribute to nutritional security. | Horticultural crops are essential for human well-being, as they provide crucial nutrients that support a balanced diet, enrich cultural landscapes, and contribute to nutritional security. To meet the demands of growing population, it is imperative to enhance both the quantity and quality of horticultural produce while considering limited stakeholder values. Integrating artificial intelligence (AI) into horticultural crop management is essential for optimizing productivity, diagnosing diseases, and enhancing value-addition. AI can help understand domestication events, adaptation processes, yield improvement, pathogen detection, and resilience by rapidly analyzing vast amounts of data from high-throughput sensors, satellite imagery, and climate models. By assimilating diverse data sets, deep learning technologies offer reliable predictive outcomes for complex and uncertain phenomena. | 72 | list | 0.820 | https://epubs.icar.org.in/index.php/IndHort/article/view/163681 | Horticultural crops are essential for human well-being, as they provide crucial nutrients that support a balanced diet, enrich cultural landscapes, and contribute to nutritional security. To meet the demands of growing population, it is imperative to enhance both the quantity and quality of horticultural produce while considering limited stakeholder values. | ||||
| What benefits do advanced technologies provide to the horticulture industry? | automated precision farming, predictive analytics, and supply chain optimization | The applicability of advanced tools and technologies for significant improvements in horticultural crops and their monitoring through sustainable practices is discussed. These technologies facilitate effective classification and recognition of images of flowers, fruits, and vegetables, as well as phenotypicidentification based on various attributes, enhancing productivity and economic returns. they leverage the horticulture industry through automated precision farming, predictive analytics, and supply chain optimization; ultimately promoting resource-efficient and sustainable practices to advance the Horticulture Sector. | 445 | list | 0.820 | https://epubs.icar.org.in/index.php/IndHort/article/view/163681 | they leverage the horticulture industry through automated precision farming, predictive analytics, and supply chain optimization; ultimately promoting resource-efficient and sustainable practices to advance the Horticulture Sector. | p[0] | 0.700 | valid | |
| What is the purpose of integrating artificial intelligence into horticultural crop management? | optimizing productivity, diagnosing diseases, and enhancing value-addition | Horticultural crops are essential for human well-being, as they provide crucial nutrients that support a balanced diet, enrich cultural landscapes, and contribute to nutritional security. To meet the demands of growing population, it is imperative to enhance both the quantity and quality of horticultural produce while considering limited stakeholder values. Integrating artificial intelligence (AI) into horticultural crop management is essential for optimizing productivity, diagnosing diseases, and enhancing value-addition. AI can help understand domestication events, adaptation processes, yield improvement, pathogen detection, and resilience by rapidly analyzing vast amounts of data from high-throughput sensors, satellite imagery, and climate models. By assimilating diverse data sets, deep learning technologies offer reliable predictive outcomes for complex and uncertain phenomena. | 453 | list | 0.820 | https://epubs.icar.org.in/index.php/IndHort/article/view/163681 | Integrating artificial intelligence (AI) into horticultural crop management is essential for optimizing productivity, diagnosing diseases, and enhancing value-addition. | ||||
| What processes can artificial intelligence help understand in horticultural crop management? | domestication events, adaptation processes, yield improvement, pathogen detection, and resilience | Horticultural crops are essential for human well-being, as they provide crucial nutrients that support a balanced diet, enrich cultural landscapes, and contribute to nutritional security. To meet the demands of growing population, it is imperative to enhance both the quantity and quality of horticultural produce while considering limited stakeholder values. Integrating artificial intelligence (AI) into horticultural crop management is essential for optimizing productivity, diagnosing diseases, and enhancing value-addition. AI can help understand domestication events, adaptation processes, yield improvement, pathogen detection, and resilience by rapidly analyzing vast amounts of data from high-throughput sensors, satellite imagery, and climate models. By assimilating diverse data sets, deep learning technologies offer reliable predictive outcomes for complex and uncertain phenomena. | 552 | list | 0.820 | https://epubs.icar.org.in/index.php/IndHort/article/view/163681 | AI can help understand domestication events, adaptation processes, yield improvement, pathogen detection, and resilience by rapidly analyzing vast amounts of data from high-throughput sensors, satellite imagery, and climate models. | ||||
| What role does artificial intelligence play in horticultural crop management? | optimizing productivity, diagnosing diseases, and enhancing value-addition. | Horticultural crops are essential for human well-being, as they provide crucial nutrients that support a balanced diet, enrich cultural landscapes, and contribute to nutritional security. To meet the demands of growing population, it is imperative to enhance both the quantity and quality of horticultural produce while considering limited stakeholder values. Integrating artificial intelligence (AI) into horticultural crop management is essential for optimizing productivity, diagnosing diseases, and enhancing value-addition. AI can help understand domestication events, adaptation processes, yield improvement, pathogen detection, and resilience by rapidly analyzing vast amounts of data from high-throughput sensors, satellite imagery, and climate models. By assimilating diverse data sets, deep learning technologies offer reliable predictive outcomes for complex and uncertain phenomena. | 453 | list | 0.820 | https://epubs.icar.org.in/index.php/IndHort/article/view/163681 | Integrating artificial intelligence (AI) into horticultural crop management is essential for optimizing productivity, diagnosing diseases, and enhancing value-addition. AI can help understand domestication events, adaptation processes, yield improvement, pathogen detection, and resilience by rapidly analyzing vast amounts of data from high-throughput sensors, satellite imagery, and climate models. | ||||
| What types of data can artificial intelligence analyze to improve horticultural crop management? | vast amounts of data from high-throughput sensors, satellite imagery, and climate models. | Horticultural crops are essential for human well-being, as they provide crucial nutrients that support a balanced diet, enrich cultural landscapes, and contribute to nutritional security. To meet the demands of growing population, it is imperative to enhance both the quantity and quality of horticultural produce while considering limited stakeholder values. Integrating artificial intelligence (AI) into horticultural crop management is essential for optimizing productivity, diagnosing diseases, and enhancing value-addition. AI can help understand domestication events, adaptation processes, yield improvement, pathogen detection, and resilience by rapidly analyzing vast amounts of data from high-throughput sensors, satellite imagery, and climate models. By assimilating diverse data sets, deep learning technologies offer reliable predictive outcomes for complex and uncertain phenomena. | 671 | list | 0.820 | https://epubs.icar.org.in/index.php/IndHort/article/view/163681 | AI can help understand domestication events, adaptation processes, yield improvement, pathogen detection, and resilience by rapidly analyzing vast amounts of data from high-throughput sensors, satellite imagery, and climate models. By assimilating diverse data sets, deep learning technologies offer reliable predictive outcomes for complex and uncertain phenomena. | ||||
| What types of images can advanced tools classify and recognize in horticultural crop monitoring? | images of flowers, fruits, and vegetables | The applicability of advanced tools and technologies for significant improvements in horticultural crops and their monitoring through sustainable practices is discussed. These technologies facilitate effective classification and recognition of images of flowers, fruits, and vegetables, as well as phenotypicidentification based on various attributes, enhancing productivity and economic returns. they leverage the horticulture industry through automated precision farming, predictive analytics, and supply chain optimization; ultimately promoting resource-efficient and sustainable practices to advance the Horticulture Sector. | 244 | list | 0.820 | https://epubs.icar.org.in/index.php/IndHort/article/view/163681 | These technologies facilitate effective classification and recognition of images of flowers, fruits, and vegetables, as well as phenotypicidentification based on various attributes, enhancing productivity and economic returns. | p[0] | 0.700 | valid | |
| Who holds the copyright for the licensed work? | Indian Council of Agricultural Research | This work is licensed under aCreative Commons Attribution-NonCommercial-ShareAlike 4. International License. Complete copyright vests with the Indian Council of Agricultural Research, who will have the right to enter into an agreement with any organization in India or abroad engaged in reprography, photocopying, storage and dissemination of information contained in it, and neither author nor his/her legal heirs will have any claims on royalty. Director:Dr Anuradha AgrawalI/c & Editor:Dr Anjani K Jha This work is licensed underAttribution-NonCommercial-NoDerivatives 4. International (CC BY-NC-ND 4.0). Dr Anuradha AgrawalI/c & Editor:Dr Anjani K Jha A spectacular flower of Western ghats Harnessing twin benefits of potato and tomato through grafting Arka Coorg Ravi and Arka Supreme as promising varieties A potential medicinal plant with commercial and trade value | 143 | factual | 0.570 | https://epubs.icar.org.in/index.php/IndHort/article/view/163681 | Complete copyright vests with the Indian Council of Agricultural Research, who will have the right to enter into an agreement with any organization in India or abroad engaged in reprography, photocopying, storage and dissemination of information contained in it, and neither author nor his/her legal heirs will have any claims on royalty. |
Read straight from the file — download or use the API URL for the full dataset.
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