Image Based Plant Disease Classification Using Deep Learning Technique
DOI:
https://doi.org/10.55524/ijirem.2023.10.3.23Keywords:
Plant diseases detection, CNN, image classification, deep learning in agricultureAbstract
As living beings, our reliance on plants and animals for sustenance is crucial. However, the available food resources cannot sustain the global population for extended periods, as only 29% of the Earth's land is suitable for sustaining the entire ecosystem. If we didn't have plant-eating bacteria or locusts, we might have enough resources to last for years. This is where my project, Plant Disease Detection and Recognition, comes into play. The main objective of this project is to identify and diagnose the diseases affecting plants, as well as determine the most effective treatments. By utilizing this system, we can obtain accurate information about the specific diseases afflicting plants. Additionally, we can identify the appropriate medications for eradicating these diseases completely. Plant diseases pose significant threats to the well-being of plants and trees, making it vital to detect them early in order to take appropriate measures. Understanding the type of disease before administering treatment is crucial. With a 92% accuracy rate, our system enables us to work on plants effectively, thereby extending their lifespan. After undergoing multiple tests, this initiative has emerged as a promising boon for humankind. Farmers, who serve as the backbone of nations, play a crucial role in our survival. Ensuring that they receive fair prices for their yields is paramount, and our system will play a significant role in achieving this. By supporting agricultural sustainability and promoting healthier crops, we can create a positive impact on food production and contribute to the overall well-being of society.
Downloads
References
M. Brahimi, K. Boukhalfa and A. Moussaoui, “Deep learning for tomato diseases: Classification and symptoms visualization,” Applied Artificial Intelligence, vol. 31, no. 4, pp. 299–315, 2017.
W. Dawei, D. Limiao, N. Jiangong, G. Jiyue, Z. Hongfei et al., “Recognition pest by image-based transfer learning,” Journal of the Science of Food and Agriculture, vol. 99, no. 10, pp. 4524– 4531, 2019.
A. Singh, P. Nath, V. Singhal, D. Anand, Kavita et al., “A new clinical spectrum for the assessment of nonalcoholic fatty liver disease using intelligent methods,” IEEE Access, vol. 8, pp. 138470–138480, 2020.
M. M. Hasan, J. P. Chopin, H. Laga and S. J. Miklavcic, “Detection and analysis of wheat spikes using convolutional neural networks,” Plant Methods, vol. 14(1), no. 100, pp. 1–13, 2018.
S. B. Patil and S. K. Bodhe, “Leaf disease severity measurement using image processing,” International Journal of Engineering and Technology, vol. 3, no. 5, pp. 297–301, 2011.
D. Oppenheim, G. Shani, O. Erlich and L. Tsror, “Using deep learning for image-based potato tuber disease detection,” Phytopathology, vol. 109, no. 6, pp. 1083–1087, 2019.
P. Jiang, Y. Chen, B. Liu, D. He and C. Liang, “Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks,” IEEE Access, vol. 7, pp. 59069–59080, 2019.
R. R. Atole and D. Park, “A multiclass deep convolutional neural network classifier for detection of common rice plant anomalies,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 1, pp. 67–70, 2018.
A. P. Singh, A. K. Luhach, S. Agnihotri, N. R. Sahu, D. S. Roy et al., “A novel patient-centric architectural framework for
blockchain-enabled healthcare applications,” IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5779–5789, 2021.
X. Zhu, M. Zhu and H. Ren, “Method of plant leaf recognition based on improved deep convolutional neural network,” Cognitive Systems Research, vol. 52, pp. 223–233, 2018.
M. Zhang, W. Li and Q. Du, “Diverse region-based CNN for hyperspectral image classification,” IEEE Transactions on Image Processing, vol. 27, no. 6, pp. 2623–2634, 2018.
A. Y. Nanehkaran, D. Zhang, J. Chen, Y. Tian and N. Al Nabhan, “Recognition of plant leaf diseases based on computer vision,” Journal of Ambient Intelligence and Humanized Computing, pp. 1–18, 2020.
S. Wan and S. Goudos, “Faster R-cNN for multi-class fruit detection using a robotic vision system,” Computer Networks, vol. 168, pp. 107036, 2020.
P. Rani, Kavita, S. Verma and G. N. Nguyen, “Mitigation of black hole and gray hole attack using swarm inspired algorithm with artificial neural network,” IEEE Access, vol. 8, pp. 121755–121764, 2020.
C. Zhao, H. Zhao, G. Wang and H. Chen, “Improvement SVM classification performance of hyperspectral image using chaotic sequences in artificial bee colony,” IEEE Access, vol. 8, pp. 73947–73956, 2020.
Downloads
Published
Issue
Section
How to Cite
