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Convolutional Neural Network-based Xception, MobileNetV2 and InceptionV3 Models for Plant Disease Identification in Sub-Saharan Africa
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 78 |
| Year of Publication: 2025 |
| Authors: Aminou Halidou, Youssoufa Mohamadou, Pascalin Tiam Apen, Daramy Vandi Von Kallon, William John Baraza, Mbouna Gildas Patrick, Djiembou Tientcheu Victor Nico, Robndoh Mardochée |
10.5120/ijca2025924642
|
Aminou Halidou, Youssoufa Mohamadou, Pascalin Tiam Apen, Daramy Vandi Von Kallon, William John Baraza, Mbouna Gildas Patrick, Djiembou Tientcheu Victor Nico, Robndoh Mardochée . Convolutional Neural Network-based Xception, MobileNetV2 and InceptionV3 Models for Plant Disease Identification in Sub-Saharan Africa. International Journal of Computer Applications. 186, 78 ( Apr 2025), 1-15. DOI=10.5120/ijca2025924642
Abstract
Plant disease identification in Sub-Saharan Africa poses a significant challenge, hindered by costly laboratory tests or subjective visual assessments. Recent advances in image-based disease identification show promise, but existing methods are limited in accuracy and efficiency. This study addresses these shortcomings by presenting a convolutional neural network (CNN)-based plant disease classifier, leveraging transfer learning from pre-trained models Xception, MobileNetV2, and InceptionV3. A high generalization rate of 98.76% is achieved in the test data, demonstrating the potential for efficient and accurate identification of plant disease. This research contributes to innovative agricultural management solutions in Sub-Saharan Africa, with implications for improving crop yields, food security, and sustainable agriculture.
References
- Valantine Achancho. Revue et analyse des stratégies nationales d’investissements et des politiques agricoles en afrique du centre: Cas du cameroun. Reconstruire le potentiel alimentaire de l’Afrique de l’Ouest, FAO/FIDA. Disponible en ligne à l’adresse suivante: http://www. fao. org/docrep/018/i3222f/i3222f04. pdf.(Consulté le 15/02/2018), 2013.
- Heba Al-Hiary, Sulieman Bani-Ahmad, M Reyalat, Malik Braik, and Zainab Alrahamneh. Fast and accurate detection and classification of plant diseases. International Journal of Computer Applications, 17(1):31–38, 2011.
- C Aggarwal Charu. Neural Networks and Deep Learning: A Textbook. Spinger, 2018.
- Junde Chen, Jinxiu Chen, Defu Zhang, Yuandong Sun, and Yaser Ahangari Nanehkaran. Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, 173:105393, 2020.
- François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251–1258, 2017.
- Chad DeChant, Tyr Wiesner-Hanks, Siyuan Chen, Ethan L Stewart, Jason Yosinski, Michael A Gore, Rebecca J Nelson, and Hod Lipson. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology, 107(11):1426–1432, 2017.
- Olivier Ezratty. Les usages de l’intelligence artificielle. 2017.
- Mercelin Francis and C Deisy. Disease detection and classification in agricultural plants using convolutional neural networks— a visual understanding. In 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), pages 1063–1068. IEEE, 2019.
- Kamlesh Golhani, Siva K Balasundram, Ganesan Vadamalai, and Biswajeet Pradhan. A review of neural networks in plant disease detection using hyperspectral data. Information Processing in Agriculture, 5(3):354–371, 2018.
- Eftekhar Hossain, Md Farhad Hossain, and Mohammad Anisur Rahaman. A color and texture based approach for the detection and classification of plant leaf disease using knn classifier. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pages 1–6. IEEE, 2019.
- Bin Hussein and school=Ziane Achour University of Djelfa Nasr al Din, year=2019. Contributions aux méthodes d’extraction de caractéristiques discriminantes pour la classification des images médicales. PhD thesis.
- Andreas Kamilaris and Francesc X Prenafeta-Boldú. Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147:70–90, 2018.
- Min Lin, Qiang Chen, and Shuicheng Yan. Network in network. arXiv preprint arXiv:1312.4400, 2013.
- Anne-Katrin Mahlein. Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant disease, 100(2):241–251, 2016.
- KP Asha Rani and S Gowrishankar. Pathogen-based classification of plant diseases: A deep transfer learning approach for intelligent support systems. IEEE Access, 11:64476–64493, 2023.
- Muhammad Hammad Saleem, Johan Potgieter, and Khalid Mahmood Arif. Plant disease detection and classification by deep learning. Plants, 8(11):468, 2019.
- Abebe Shimeles, Audrey Verdier-Chouchane, and Amadou Boly. Building a resilient and sustainable agriculture in sub- Saharan Africa. Springer Nature, 2018.
- Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9, 2015.
- Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826, 2016.
- Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105–6114. PMLR, 2019.
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