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.


CNN Classifier, Deep Neural Network, Image Classification, Machine Learning, Groundnut, Groundnut Diseases Detection,


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  1. C. Pazderka, A. Emmott, Chatham House Procurement for Development Forum: Groundnuts Case Study. Chatham house, 10. (2010).
  2. P. Singh, S. Nedumaran, B.R. Ntare, K.J. Boote, N.P. Singh, K. Srinivas, M.C.S. Banti-lan, Potential benefits of drought and heat tolerance in groundnut for adaptation to climate change in India and West Africa. Mitigation and adaptation strategies for global change, 19, (2014). 509-529. https://doi.org/10.1007/s11027-012-9446-7
  3. M.D.M. Kadiyala, S. Nedumaran, J. Padmanabhan, M.K. Gumma, S. Gummadi, S.R. Srigiri, R. Robertson, A. Whitbread, Modeling the potential impacts of climate change and adaptation strategies on groundnut production in India. Science of the Total Environment, 776 (2021), 145996. https://doi.org/10.1016/j.scitotenv.2021.145996
  4. A. Hafeez, M.A. Husain, S.P. Singh, A. Chauhan, M.T. Khan, N. Kumar, A. Chauhan, S.K. Soni, Implementation of drone technology for farm monitoring & pesticide spraying: A review. Information processing in Agriculture, 10, (2022) 192-203. https://doi.org/10.1016/j.inpa.2022.02.002
  5. M. Dhanaraju, P. Chenniappan, K. Ramalingam, S. Pazhanivelan, R. Kaliaperumal, Smart farming: Internet of Things (IoT)-based sustainable agriculture. Agriculture, 12(10), (2022), 1745. https://doi.org/10.3390/agriculture12101745
  6. A. Gabriel, M. Gandorfer, Adoption of digital technologies in agriculture-an inventory in a european small-scale farming region. Precision Agriculture, 24(1), (2023), 68-91. https://doi.org/10.1007/s11119-022-09931-1
  7. J.C. Saha, A study on oilseed economy of India. Indian Journal of Agricultural Marketing, 37(1), (2023), 74-94.
  8. Annual report 2020-21, Department of Agriculture, cooperation and farmers welfare, Ministry of Agriculture and Farmers welfare, Government of India. https://agricoop.nic.in/Documents/annual-report-2020-21.pdf
  9. Groundnut Crop Survey Reports APEDA, Government of India. 2017-2020, https://apeda.gov.in/apedawebsite/GroundNut/GroundNut.htm
  10. V.J. Naik, C. Umesha, Effect of organic manures and bio-fertilizers on growth and yield of Groundnut (Arachis hypogaea L.), The Pharma Innovation Journal, 11(5), (2022), 1249-1251.
  11. S.K. Bera, K. Rani, J.H. Kamdar, M.K. Pandey, H. Desmae, C.C. Holbrook, M.D. Burow, N. Manivannan, R.S. Bhat, M.D. Jasani, S.S. Bera, A.M. Badigannavar, G. Sunkad, G.C. Wright, P. Janila, R.K. Varshney, (2022). Genomic Designing for Biotic Stress Resistant Peanut. In Genomic Designing for Biotic Stress Resistant Oilseed Crops, Cham: Springer. 137-214. https://doi.org/10.1007/978-3-030-91035-8_4
  12. S. Young, The Future of Farming: Artificial Intelligence and Agriculture. Harvard International Review, 41(1), (2020), 45-47.
  13. E.A. Abioye, O. Hensel, T.J. Esau, O. Elijah, M.S.Z. Abidin, A.S. Ayobami, O. Yerima, A. Nasirahmadi, Precision irrigation management using machine learning and digital farm-ing solutions. AgriEngineering, 4(1), (2022), 70-103. https://doi.org/10.3390/agriengineering4010006
  14. U.P. Singh, S.S. Chouhan, S. Jain, S. Jain, Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease. IEEE Access, 7, (2019), 43721-43729. https://doi.org/10.1109/ACCESS.2019.2907383
  15. J. Li, C. Xu, L. Jiang, Y. Xiao, L. Deng, Z. Han, Detection and Analysis of Behavior Trajectory for Sea Cucumbers Based on Deep Learning. IEEE Access, 8, (2020), 18832–18840. https://doi.org/10.1109/ACCESS.2019.2962823
  16. Y. Ai, C. Sun, J. Tie, X. Cai, Research on recognition model of crop diseases and insect pests based on deep learning in harsh environments. IEEE Access, 8, (2020), 171686–171693. https://doi.org/10.1109/ACCESS.2020.3025325
  17. D. Jiang, F. Li, Y. Yang, S. Yu, (2020) A tomato leaf diseases classification method based on deep learning. In 2020 chinese control and decision conference (CCDC) IEEE. China. https://doi.org/10.1109/CCDC49329.2020.9164457
  18. R.V. Meeradevi, M.R. Mundada, S.P. Sawkar, R.S. Bellad, P.S. Keerthi, Design and Development of Efficient Techniques for Leaf Disease Detection using Deep Convolutional Neural Networks. 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, IEEE, India. https://doi.org/10.1109/DISCOVER50404.2020.9278067
  19. S.V. Militante, B.D. Gerardo, R.P. Medina, Sugarcane disease recognition using deep learning. In 2019 IEEE Eurasia conference on IOT, communication and engineering (ECICE), IEEE, Taiwan. https://doi.org/10.1109/ECICE47484.2019.8942690
  20. U. Barman, D. Sahu, G.G. Barman, J. Das, Comparative assessment of deep learning to detect the leaf diseases of potato based on data augmentation. In 2020 International Conference on Computational Performance Evaluation (ComPE), 682-687. IEEE https://doi.org/10.1109/ComPE49325.2020.9200015
  21. I.Z. Mukti, D. Biswas, (2019). Transfer learning based plant diseases detection using ResNet50. In 2019 4th International conference on electrical information and communication technology (EICT) 1-6. IEEE. https://doi.org/10.1109/EICT48899.2019.9068805
  22. U. Barman, R.D. Choudhury, D. Sahu, G.G. Barman, (2020) Comparison of convo-lution neural networks for smartphone image based real time classification of citrus leaf disease. Computers and Electronics in Agriculture, 177, (2020), 105661. https://doi.org/10.1016/j.compag.2020.105661
  23. H. Sun, H. Xu, B. Liu, D. He, J. He, H. Zhang, N. Geng, (2021) MEAN-SSD: A novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks. Computers and Electronics in Agriculture, 189, (2021), 106379. https://doi.org/10.1016/j.compag.2021.106379
  24. H. Kukadiya, D. Meva, (2022) Automatic Cotton Leaf Disease Classification and Detection by Convolutional Neural Network. In International Conference on Advancements in Smart Computing and Information Security, Springer, Cham. https://doi.org/10.1007/978-3-031-23092-9_20
  25. P. Bir, R. Kumar, G. Singh, Transfer learning-based tomato leaf disease detection for mobile applications. 2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON. IEEE, India. https://doi.org/10.1109/GUCON48875.2020.9231174
  26. J.F.V. Oraño, E.A. Maravillas, C.J.G. Aliac, Jackfruit Fruit Damage Classification using Convolutional Neural Network. In 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), IEEE, Philippines. https://doi.org/10.1109/HNICEM48295.2019.9073341
  27. R.S. Latha, G.R. Sreekanth, R.C. Suganthe, R. Rajadevi, S. Karthikeyan, S. Kanivel, B. Inbaraj, Automatic detection of tea leaf diseases using deep convolution neural net-work. In 2021 International Conference on Computer Communication and Informatics (ICCCI), IEEE, India. https://doi.org/10.1109/ICCCI50826.2021.9402225
  28. S. Bhowmik, A.K. Talukdar, K. Kumar Sarma, Detection of Disease in Tea Leaves Using Convolution Neural Network. International Conference on Advanced Communication Technologies and Signal Processing, IEEE, India. https://doi.org/10.1109/ACTS49415.2020.9350413
  29. W. Haider, A. Ur Rehman, A. Maqsood, S.Z. Javed, Crop Disease Diagnosis using Deep Learning Models. 2020 Global Conference on Wireless and Optical Technologies, GCWOT, IEEE, Spain. https://doi.org/10.1109/GCWOT49901.2020.9391605
  30. V. Kumar, P.P. Thirumalaisamy, (2016). Diseases of groundnut. Disease of field crops and their management. Indian Phytopathological Society, Today and Tomorrow’s Printers and Publishers, New Delhi, 459-487.