Fine-tuning pre-trained deep learning models for crop prediction using soil conditions in smart agriculture.
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| Title: | Fine-tuning pre-trained deep learning models for crop prediction using soil conditions in smart agriculture. |
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| Authors: | Pawaskar, Praveen1,2 praveenpawaskar555@gmail.com, H. K., Yogish1 yogishhk@gmail.com, B., Pakruddin2 fakrubasha@gmail.com, Yogish, Deepa3 deepa.yogish@christuniversity.in |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Dec2025, Vol. 15 Issue 6, p5667-5678. 12p. |
| Subjects: | Soils, Soil classification, Agricultural forecasts, Classification, Deep learning, Agricultural technology |
| Abstract: | Agriculture is the backbone of the Indian economy, with soil quality playing a crucial role in crop productivity. Farmers often struggle to select the appropriate crop based on soil type, leading to significant losses in yield and productivity. To address this challenge, deep learning techniques provide an efficient solution for automated soil classification. In this study, a dataset of 781 original soil images, including clay soil, alluvial soil, red soil, and black soil, was collected from Kaggle and augmented to 3,702 images to enhance model training. Several deep learning models were employed for soil classification, including pretrained architectures and a proposed model, SoilNet. Experimental results demonstrated that DenseNet201 achieved 100% validation accuracy, ResNet50V2 98%, VGG16 99%, MobileNetV2 99%, and the proposed SoilNet model 97%. The proposed approach outperformed existing work by surpassing 95% accuracy. Additionally, model performance was evaluated using precision, recall, and F1-score, ensuring a comprehensive analysis of classification effectiveness. These findings highlight the potential of deep learning in improving soil classification accuracy, aiding farmers in making informed crop selection decisions. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 190950299 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Fine-tuning pre-trained deep learning models for crop prediction using soil conditions in smart agriculture. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pawaskar%2C+Praveen%22">Pawaskar, Praveen</searchLink><relatesTo>1,2</relatesTo><i> praveenpawaskar555@gmail.com</i><br /><searchLink fieldCode="AR" term="%22H%2E+K%2E%2C+Yogish%22">H. K., Yogish</searchLink><relatesTo>1</relatesTo><i> yogishhk@gmail.com</i><br /><searchLink fieldCode="AR" term="%22B%2E%2C+Pakruddin%22">B., Pakruddin</searchLink><relatesTo>2</relatesTo><i> fakrubasha@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Yogish%2C+Deepa%22">Yogish, Deepa</searchLink><relatesTo>3</relatesTo><i> deepa.yogish@christuniversity.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Dec2025, Vol. 15 Issue 6, p5667-5678. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Soils%22">Soils</searchLink><br /><searchLink fieldCode="DE" term="%22Soil+classification%22">Soil classification</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+forecasts%22">Agricultural forecasts</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+technology%22">Agricultural technology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Agriculture is the backbone of the Indian economy, with soil quality playing a crucial role in crop productivity. Farmers often struggle to select the appropriate crop based on soil type, leading to significant losses in yield and productivity. To address this challenge, deep learning techniques provide an efficient solution for automated soil classification. In this study, a dataset of 781 original soil images, including clay soil, alluvial soil, red soil, and black soil, was collected from Kaggle and augmented to 3,702 images to enhance model training. Several deep learning models were employed for soil classification, including pretrained architectures and a proposed model, SoilNet. Experimental results demonstrated that DenseNet201 achieved 100% validation accuracy, ResNet50V2 98%, VGG16 99%, MobileNetV2 99%, and the proposed SoilNet model 97%. The proposed approach outperformed existing work by surpassing 95% accuracy. Additionally, model performance was evaluated using precision, recall, and F1-score, ensuring a comprehensive analysis of classification effectiveness. These findings highlight the potential of deep learning in improving soil classification accuracy, aiding farmers in making informed crop selection decisions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v15i6.pp5667-5678 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 5667 Subjects: – SubjectFull: Soils Type: general – SubjectFull: Soil classification Type: general – SubjectFull: Agricultural forecasts Type: general – SubjectFull: Classification Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Agricultural technology Type: general Titles: – TitleFull: Fine-tuning pre-trained deep learning models for crop prediction using soil conditions in smart agriculture. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pawaskar, Praveen – PersonEntity: Name: NameFull: H. K., Yogish – PersonEntity: Name: NameFull: B., Pakruddin – PersonEntity: Name: NameFull: Yogish, Deepa IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 15 – Type: issue Value: 6 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
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