Enhanced Convolutional Neural Network for Accurate Crop Recommendation System on Climate Data.
Agriculture is crucial for economic growth and development, yet crop productivity is frequently undermined by improper crop selection and ineffective identification of crop types. Traditional systems often focus on isolated factors, such as weather or soil conditions, which leads to less accurate cr...
| Published in: | SHS Web of Conferences Vol. 216; pp. 1 - 15 |
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| Main Authors: | , , |
| Format: | Article |
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EDP Sciences
5/23/2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=185448780&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 185448780 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 24165182 FT5R jtl: SHS Web of Conferences issn: 24165182 maglogo: N pubinfo: dt: 5/23/2025 vid: 216 pid: 76090 pub: EDP Sciences artinfo: ui: 185448780 10.1051/shsconf/202521601041 ppf: 1 ppct: 14 formats: tig: atl: Enhanced Convolutional Neural Network for Accurate Crop Recommendation System on Climate Data. aug: au: Adnan, Myasar M. AI_Sadi, Hafidh l. Abhilash, Pideka Kundil affil: Department of computers Techniques engineering, College of technical engineering, The Islamic University of Najaf, Iraq The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq The Islamic University of Babylon, Babylon, Iraq College of MLT, Ahl Al Bayt University, karbala, Iraq Department of Information Technoldgy, Gokaraju Rangaraju Institute of Engineering and Technology JNTUH, Bachupally, Hyderabad, India sug: ab: Agriculture is crucial for economic growth and development, yet crop productivity is frequently undermined by improper crop selection and ineffective identification of crop types. Traditional systems often focus on isolated factors, such as weather or soil conditions, which leads to less accurate crop suitability predictions. This research addresses these challenges by developing a robust crop recommendation system that integrates multiple factors for improved accuracy. This research aims to develop a robust crop recommendation system by addressing these limitations. We propose a comprehensive approach that includes preprocessing with the Min-Max Normalization algorithm and feature selection using an Enhanced Cuckoo Search Optimization Algorithm (ECSO). The chosen features are classified and Improved Convolutional Neural Network (ICNN) algorithm predicts crops accurately. Our model, combining the CS-ICNN framework, offers enhanced recommendations by considering both soil-specific characteristics and environmental factors. Experimental results demonstrate that the proposed CS-ICNN approach achieves superior accuracy, precision, recall, and reduced execution time compared to existing methodologies. pubtype: Conference Proceedings doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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