Effective Groundnut Crop Management by Early Prediction of Leaf Diseases through Convolutional Neural Networks
- Title
- Effective Groundnut Crop Management by Early Prediction of Leaf Diseases through Convolutional Neural Networks
- Creator
- Kukadiya H.; Meva D.; Arora N.; Srivastava S.
- Description
- 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.
- Source
- International Research Journal of Multidisciplinary Technovation, Vol-6, No. 1, pp. 17-31.
- Date
- 2024-01-01
- Publisher
- Asian Research Association
- Subject
- CNN Classifier; Deep Neural Network; Groundnut; Groundnut Diseases Detection; Image Classification; Machine Learning
- Coverage
- 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
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 25821040
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Kukadiya H.; Meva D.; Arora N.; Srivastava S., “Effective Groundnut Crop Management by Early Prediction of Leaf Diseases through Convolutional Neural Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/13314.