Deep learning-based crop health enhancement through early disease prediction
Manual disease detection methods currently in use are laborious, time-intensive, and heavily reliant on specialized knowledge. The urgent need to address these challenges motivates this study. The primary goal of this research is to develop a model capable of accurately distinguishing between health...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Cogent Food & Agriculture |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311932.2024.2423244 |
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| author | Venkata Santhosh Yakkala Krishna Vamsi Nusimala Badisa Gayathri Sriya Kanamarlapudi S. S. Aravinth Ayodeji Olalekan Salau S. Srithar |
| author_facet | Venkata Santhosh Yakkala Krishna Vamsi Nusimala Badisa Gayathri Sriya Kanamarlapudi S. S. Aravinth Ayodeji Olalekan Salau S. Srithar |
| author_sort | Venkata Santhosh Yakkala |
| collection | DOAJ |
| description | Manual disease detection methods currently in use are laborious, time-intensive, and heavily reliant on specialized knowledge. The urgent need to address these challenges motivates this study. The primary goal of this research is to develop a model capable of accurately distinguishing between healthy and diseased crop leaves. Additionally, the model aims to identify specific diseases affecting the crops if they are found to be diseased. Leveraging the power of machine learning algorithms, particularly Convolutional Neural Networks (CNNs) and ResNet-9 architecture, this research seeks to transform the process of detecting plant diseases. It focuses on analyzing diverse morphological features such as color, intensity, and dimensions in plant leaves to enable quick and accurate classification. By introducing AI-driven systems into agricultural practices, this study aims to revolutionize disease identification, prediction, and management. The overarching objective is to minimize crop losses and enhance agricultural productivity. In addition to highlighting the significance of machine learning techniques such as ResNet-9, this study emphasizes the importance of environmentally friendly biological control methods for regulating pests and diseases in agricultural settings. The adoption of Convolutional Neural Networks, specifically the ResNet-9 architecture, signifies a significant advancement in predicting plant diseases. This approach holds the promise of vastly improving the accuracy and efficiency of disease forecasting within the agricultural domain. |
| format | Article |
| id | doaj-art-8f922f2b16d34852b29fa1a0a271a6e4 |
| institution | DOAJ |
| issn | 2331-1932 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Food & Agriculture |
| spelling | doaj-art-8f922f2b16d34852b29fa1a0a271a6e42025-08-20T02:40:35ZengTaylor & Francis GroupCogent Food & Agriculture2331-19322025-12-0111110.1080/23311932.2024.2423244Deep learning-based crop health enhancement through early disease predictionVenkata Santhosh Yakkala0Krishna Vamsi Nusimala1Badisa Gayathri2Sriya Kanamarlapudi3S. S. Aravinth4Ayodeji Olalekan Salau5S. Srithar6Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, IndiaDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, IndiaDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, IndiaDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, IndiaDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, IndiaDepartment of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, NigeriaDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, IndiaManual disease detection methods currently in use are laborious, time-intensive, and heavily reliant on specialized knowledge. The urgent need to address these challenges motivates this study. The primary goal of this research is to develop a model capable of accurately distinguishing between healthy and diseased crop leaves. Additionally, the model aims to identify specific diseases affecting the crops if they are found to be diseased. Leveraging the power of machine learning algorithms, particularly Convolutional Neural Networks (CNNs) and ResNet-9 architecture, this research seeks to transform the process of detecting plant diseases. It focuses on analyzing diverse morphological features such as color, intensity, and dimensions in plant leaves to enable quick and accurate classification. By introducing AI-driven systems into agricultural practices, this study aims to revolutionize disease identification, prediction, and management. The overarching objective is to minimize crop losses and enhance agricultural productivity. In addition to highlighting the significance of machine learning techniques such as ResNet-9, this study emphasizes the importance of environmentally friendly biological control methods for regulating pests and diseases in agricultural settings. The adoption of Convolutional Neural Networks, specifically the ResNet-9 architecture, signifies a significant advancement in predicting plant diseases. This approach holds the promise of vastly improving the accuracy and efficiency of disease forecasting within the agricultural domain.https://www.tandfonline.com/doi/10.1080/23311932.2024.2423244Agroecosystem resiliencephyto pathological analysismultispectral imagingprecision agriculturefeature engineeringanomaly detection |
| spellingShingle | Venkata Santhosh Yakkala Krishna Vamsi Nusimala Badisa Gayathri Sriya Kanamarlapudi S. S. Aravinth Ayodeji Olalekan Salau S. Srithar Deep learning-based crop health enhancement through early disease prediction Cogent Food & Agriculture Agroecosystem resilience phyto pathological analysis multispectral imaging precision agriculture feature engineering anomaly detection |
| title | Deep learning-based crop health enhancement through early disease prediction |
| title_full | Deep learning-based crop health enhancement through early disease prediction |
| title_fullStr | Deep learning-based crop health enhancement through early disease prediction |
| title_full_unstemmed | Deep learning-based crop health enhancement through early disease prediction |
| title_short | Deep learning-based crop health enhancement through early disease prediction |
| title_sort | deep learning based crop health enhancement through early disease prediction |
| topic | Agroecosystem resilience phyto pathological analysis multispectral imaging precision agriculture feature engineering anomaly detection |
| url | https://www.tandfonline.com/doi/10.1080/23311932.2024.2423244 |
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