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 Articles Article Faculty Publications -Articles 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 Effective Groundnut Crop Management by Early Prediction of Leaf Diseases through Convolutional Neural Networks Subject The topic of the resource CNN Classifier; Deep Neural Network; Groundnut; Groundnut Diseases Detection; Image Classification; Machine Learning Description An account of the resource Groundnut (Arachis hypogaea L.), is the sixth-most significant leguminous oilseed crop grown all over worldwide. Groundnut, due to its high content of various dietary fibers, is classified as a valuable cash, staple and a feed crop for millions of households around the world. However, due to varied environmental factors, the crop is quite prone to many kinds of diseases, identifiable through its leaves, for which Groundnut producers have to suffer major losses every year. An early detection of such diseases is essential in order to save this significant crop and avoid huge losses. This paper presents a novel Machine Learning based Deep Convolution Neural Network (CNN) model CNN8GN. The model uses transfer learning technique for detection of such diseases in Groundnuts at an early stage of crop production. A Groundnut real image data set containing a total of 5322 real images for six different classes of Groundnut leaf diseases, captured in the fields of Gujarat state (India) during September 2022 to February 2023, is generated for training, testing and evaluation of the proposed model. The proposed deep learning model architecture is designed on eight different layers and can be used on varied sized images using simple ReLu and Softmax activation functions. The performance of the proposed CNN8GN model on Groundnut real image dataset is examined using a detailed experimental analysis with other six pre-trained models: VGG16, InceptionV3, Resnet50, ResNet152V2, VGG19, and MobileNetV2. CNN8GN results are also examined in detail using different sets of input parameters values. The proposed model has shown significant improvements for disease detection in comparative analysis with 99.11% training and 91.25% testing accuracy. The Author(s) 2024. Creator An entity primarily responsible for making the resource Kukadiya H.; Meva D.; Arora N.; Srivastava S. Source A related resource from which the described resource is derived International Research Journal of Multidisciplinary Technovation, Vol-6, No. 1, pp. 17-31. Publisher An entity responsible for making the resource available Asian Research Association 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/10.54392/irjmt2412" target="_blank" rel="noreferrer noopener">https://doi.org/10.54392/irjmt2412</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183627112&amp;doi=10.54392%2Firjmt2412&amp;partnerID=40&amp;md5=e2047d7d58ce36efc7353ac9d72c07a8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183627112&amp;doi=10.54392%2firjmt2412&amp;partnerID=40&amp;md5=e2047d7d58ce36efc7353ac9d72c07a8</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 25821040 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 Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kukadiya H., Department of Computer Application, Marwadi University, Gujarat, Rajkot, 360003, India; Meva D., Department of Computer Application, Marwadi University, Gujarat, Rajkot, 360003, India; Arora N., Department of Computer Science, Kalindi College, University of Delhi, Delhi, 110005, India; Srivastava S., School of Sciences, Christ (Deemed to be University), Ghaziabad, Delhi, 201003, India