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dc.contributor.authorAgam Kumar, 21SCSE1100048
dc.contributor.authorShirsh Yadav, 21SCSE1100052
dc.date.accessioned2024-09-17T08:56:52Z
dc.date.available2024-09-17T08:56:52Z
dc.date.issued2023-05
dc.identifier.urihttp://10.10.11.6/handle/1/18045
dc.descriptionSCHOOL OF COMPUTER APPLICATION AND TECHNOLOGY DEPARTMENT OF COMPUTERAPPLICATION GALGOTIAS UNIVERSITY, GREATER NOIDA INDIAen_US
dc.description.abstractAgriculture is the backbone of many economies worldwide, providing food security and livelihoods for millions of people. However, it faces challenges such as climate change, soil degradation, and fluctuating market demands. To address these challenges and promote sustainable agriculture, we propose the development of a Crop Recommendation System (CRS). The Crop Recommendation System is an intelligent software solution that leverages advanced data analytics and machine learning techniques to assist farmers in making informed decisions about crop selection. By analyzing a combination of factors, including soil health, weather patterns, historical crop performance, and market trends, the CRS aims to recommend the most suitable crop varieties for a specific region and time. The Crop Recommendation System aims to empower farmers with data-driven insights, reduce the guesswork in crop selection, increase agricultural productivity, and contribute to more sustainable and efficient farming practices. By harnessing the power of machine learning and data analytics, this system has the potential to revolutionize crop planning and decision-making in agriculture, ultimately improving food production and farmer livelihoodsen_US
dc.language.isoen_USen_US
dc.publisherGalgotias Universityen_US
dc.subjectCrop Recommendation Systemen_US
dc.subjectusing Nature inspireden_US
dc.subjectoptimization Algorithmen_US
dc.titleCrop Recommendation System using Nature inspired optimization Algorithmen_US
dc.typeTechnical Reporten_US


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