Paper Title
Anticipate Crop Price And Mitigate Risk In Farming Business
Article Identifiers
: Keywords
crop price prediction, explore commodities, Decision tree regression
Abstract
The agriculture sector plays a crucial role in supporting livelihoods and employment in densely populated nations like India, serving as a foundational pillar of the economy. However, this sector faces challenges from climate change and volatile market prices, leading to crop damage and significant financial losses. To tackle these issues, it's essential to enhance farmers' awareness of crop management techniques, climate trends, and market dynamics. One promising solution is leveraging machine learning, particularly Decision Tree Regression algorithms, for price forecasting. By using these algorithms, a dedicated platform can offer farmers tailored price prediction models, enabling them to make well-informed decisions about crop cultivation and marketing strategies. Additionally, this explore commodity platform can provide farmers with real time crop prices, optimal locations for cultivation, seasonal planting advice, and insights into export opportunities. By consolidating these features, the platform becomes an invaluable tool for farmers, equipping them with the data-driven insights needed to optimize their agricultural practices. This approach promotes a more streamlined and informed agricultural sector, benefiting both farmers and consumers.
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How To Cite (APA)
Thumula Akshay Kumar, Thipparthi Sumanth, Sankatala Harshitha, & Chindham Sri Vidya (May-2024). Anticipate Crop Price And Mitigate Risk In Farming Business . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), g629-g637. https://ijnrd.org/papers/IJNRD2405674.pdf
Issue
Volume 9 Issue 5, May-2024
Pages : g629-g637
Other Publication Details
Paper Reg. ID: IJNRD_222483
Published Paper Id: IJNRD2405674
Downloads: 000122253
Research Area: Computer Science & TechnologyÂ
Author Type: Indian Author
Country: Karimnagar, Telangana, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2405674.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405674
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