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 Conference Papers Conference Paper Faculty Publications- Conference Papers 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 Pixels to Pathogens: A Deep Learning Approach to Plant Pathology Detection Subject The topic of the resource Deep Learning; EfficientNetB3; Mobile Application; Pathology detection; Plant Disease; Regularization; Transfer Learning Description An account of the resource It is known that accurately identifying, early and timely treatment and elimination of the plant diseases is essential for crop protection and healthy crop growth. In traditional or conventional methods, identification and classification were done by testing in laboratories or through visual inspection by farmers. Now going through the testing in labs is very time consuming, while the visual inspection requires enough experience and knowledge. To solve this problem, our study proposes a robust plant pathogen detection method based on a Deep Learning approach on a large dataset containing about 38 categories of different species like Maize, Potatoes, Tomatoes, Bell Pepper, Peach, Strawberry etc. and diseases like rust, molds, blight (late and early). This crop disease detection model leverages the power of the EfficientNetB3 architecture, a state-of-art convolutional neural network(CNN). The main backbone is served by EfficientNetB3and then it is fine-tuned using different hyperparameters and other regularization techniques like weight decay, dropout method and optimizers like RAdam,to enhance the overall accuracy coupled with dynamic learning rate adjustment. In the testing set of the dataset, the proposed model shows encouraging accuracy of about 99.25%, high precision of about 97.35%. A thorough evaluation of the model's functionality is given by the help of training and validation line chart and loss chart that gives the in-depth information on the prediction. And then we implemented the detection model in our mobile application whose interface screen shots are given below. In the application the image can be taken by camera or fed from folders and it will detect the type of disease. 2024 IEEE. Creator An entity primarily responsible for making the resource Saha A.; Sharma V.; Mondal R.; Mishra S.; Daramola I.; Abbas A. Source A related resource from which the described resource is derived 4th International Conference on Innovative Practices in Technology and Management 2024, ICIPTM 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICIPTM59628.2024.10563558" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICIPTM59628.2024.10563558</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197703419&amp;doi=10.1109%2FICIPTM59628.2024.10563558&amp;partnerID=40&amp;md5=bef815f71481fca9e09ee6ceea3cc290" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197703419&amp;doi=10.1109%2fICIPTM59628.2024.10563558&amp;partnerID=40&amp;md5=bef815f71481fca9e09ee6ceea3cc290</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835030775-7 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Saha A., Kalinga Institute of Industrial Technology, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Mondal R., Kalinga Institute of Industrial Technology, India; Mishra S., Kalinga Institute of Industrial Technology, India; Daramola I., University of Bolton, School of Creative Technologies, United Kingdom; Abbas A., University of Bolton, School of Creative Technologies, United Kingdom