Authors : Ashadu Jaman Shawon; Oishi Singh; Tasrina Sarkar; Kazi Faiz Ahmed Sadnan

Volume/Issue : Volume 9 - 2024, Issue 10 - October

Google Scholar : https://tinyurl.com/bde6kbub

Scribd : https://tinyurl.com/3yyjwmmp

DOI : https://doi.org/10.38124/ijisrt/IJISRT24OCT252

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Plant diseases represent a serious threat to national productivity and global food security. Effective therapy for multiple diseases requires a precise and useful differentiation of them. In this work, a computerized system for the identification and categorization of diseases in potato and maize crops is developed using convolutional neural networks. The demonstration was created with the ResNet50V2 model and tested on a combined collection of images of leaves. The system achieved an astounding accuracy of 85.19. Enhancing model execution through exchange learning, fine-tuning, and information augmentation were all part of the process. With the use of another dataset, the trained model was verified and produced positive results, almost exactly differentiating between the disease-causing leaf type (potato or maize). This technology helps ranchers adopt sustainable and knowledgeable disease management methods by promoting timely mediations, which in turn advances disease discovery.

Keywords : Potato, Maize, Leaf Disease, Machine Learning, CNN Model.

References :

1. Barbedo JGA. Plant disease identification from individual lesions and spots using deep learning. . Biosyst Eng 2019; 180: 96–107.
2. Ferentinos KP. Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 2018; 145: 311–318.
3. Mohanty SP, HDP, & SM. Using deep learning for image-based plant disease detection. Front Plant Sci 2016; 7.
4. Sladojevic S, AM, AA, CD, & SD. Deep neural networks based recognition of plant diseases by leaf image classification. Comput Electron Agric 2016; 128: 88–95.
5. Ramcharan A, BK, MP, AB, LJ, & HDP. Deep learning for image-based cassava disease detection. Front Plant Sci 2017; 8.
6. Selvaraj MG, VA, RH, SN, ES, OW, & BG. AI-powered banana diseases and pest detection. Plant Methods 2019; 15: 1–11.
7. Brahimi M, BK, & MA. Deep learning for tomato diseases. Classification and symptoms visualization Applied Artificial Intelligence 2017; 31: 299–315.
8. Artzet S, WTS, & RCM. Multimodel approach to resolving banana plant diseases and pest defects using machine learning algorithms. Plant Dis 2019; 103: 2263–2277.
9. Kour VP, & AS. Automated detection of diseases in pomegranate plant. Multimed Tools Appl 2020; 79: 16499–16517.
10. Coulibaly S, K-FB, DD, & CB. Deep learning for millet head detection and counting in visual images. Comput Electron Agric 2019; 165.
11. Too EC, YL, NS, & YL. A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric 2019; 161: 272–279.
12. Polder G, BPM, de VHA, van der WJM, & KJ. Potato virus Y detection in seed potato crops using deep learning on hyperspectral images. Front Plant Sci 2019; 10.
13. Nagasubramanian K, JS, SS, SAK, SA, & GB. Plant disease identification using explainable 3D deep learning on hyperspectral images. . Plant Methods 2019; 15.
14. Qin J, BTF, RMA, & BWG. Detection of citrus huanglongbing using visible and near-infrared spectroscopy. Comput Electron Agric 2019; 158: 161–174.
15. Barbedo JGA. Plant disease identification from individual lesions and spots using deep learning. Biosyst Eng 2018; 180: 96–107.
16. Kamilaris A, & P-BFX. Deep learning in agriculture: A survey. Comput Electron Agric 2018; 147: 70–90.
17. Singh A, GB, SAK, & SS. Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci 2018; 23: 810–824.
18. Srivastava N, HG, KA, SI, & SR. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 2014; 15: 1929–1958.
19. King G, & ZL. Logistic regression in rare events data. Political Analysis 2001; 9: 137–163.
20. Shawon AJ, TA, & MR. Emotion Detection Using Machine Learning: An Analytical Review. Malaysian Journal of Science and Advanced Technology 2024; 4: 32–43.