Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singh, Jaspreet; Tanwar, Shashi Title A name given to the resource Leveraging ResNeXt50 and LSTM for Enhanced Plant Disease Detection: A Hybrid Model Proposal Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Emerging Smart Agricultural Practices Using Artificial Intelligence;pp.231-246 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394274277.ch11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394274277.ch11</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105016280671?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016280671?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singh J., Computer Science Department, Christ University, Uttar Pradesh, Ghaziabad, India; Tanwar S., Computer Science &amp; Engineering, Anangpuria School of Management &amp; Technology, Haryana, Faridabad, India Description An account of the resource This chapter explores the implementations of deep learning algorithms along with remote sensing technologies for precise identification and categorization of plant diseases, focusing on enhancing accuracy and efficiency in agricultural practices. This research study intends to succeed in building a hybrid model for the classification and forecasting of diseased plants with high accuracy. Plant disease detection and classification is a critical field of study within agricultural science and technology. It involves identifying and categorizing diseases affecting plants to ensure timely and effective management practices. Early and accurate identification of plant diseases is crucial to minimize crop loss, maintain food security, and reduce the use of pesticides, which can have adverse environmental and health effects. In any country, both the yield and the quality of agricultural products are essential for the success of agriculture. Plant disease (i.e. abnormal growth or functionality) detection is tough work, which has prompted numerous investigators to apply image processing, machine learning (ML), computer vision, and big data analytics, etc., techniques, which make the challenging assignment easier. The proposed approach integrates the deep convolutional neural network ResNeXt50 with long short-term memory (LSTM) networks to tackle the dual tasks of plant leaf disease classification and segmentation. The ResNeXt50 backbone extracts intricate spatial features from plant leaf images, while the LSTM component models the temporal dynamics of disease progression. This hybrid model exploits the hierarchical feature representation of ResNeXt50 and the sequential learning capabilities of LSTM to enhance accuracy and contextual understanding of plant leaf diseases. The model's training accuracy was enhanced to a maximum of 99.74% and a validation accuracy of 95.44%, scoring 94% in F1, 96% in recall, and 96% in accuracy. Comparative analysis reveals that the ResNeXt50 + LSTM model outperforms other classifiers, including Inception V3, AlexNet, ResNet50, and VGG16, addressing overfitting and vanishing gradient issues. The model demonstrates superior performance in handling imbalanced data and excels in plant disease prediction, validated through various benchmarks and datasets. This study confirms the hybrid model's robustness and potential for practical application in plant pathology. 2025 by The Institute of Electrical and Electronics Engineers, Inc. Subject The topic of the resource CNN; deep learning; machine learning; plant disease classification; ResNextModel Publisher An entity responsible for making the resource available wiley Relation A related resource ISBN: 978-139427427-7; 978-139427424-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online