Embedded Files
Contents
Contents
Introduction
Motivation
Objectives
Related Works
Methodology
Dataset
Result Analysis
Limitations
Conclusion
References
Introduction
Introduction
Corn Disease Detection
Corn production & standard increasing
Decreasing the need of corn
Stop spreading or outbreaking the disease among trees
Monitoring the growth & more sustainable corn
Motivation
Motivation
Balancing the market need
Minimizing the natural imbalance of production
Food quality & product quality ensuring
With the product quality also keeping the sustainability
Creating more direction for future research
Objective
Objective
Develop a better method to detect disease
Improve root level grazier awareness
Create an interconnective field for related worker
Spreading the use of new tech to detect disease
Related Works
Related Works
01. Previous research has primarily focused on conventional image processing methods for pathogen detection in plants.
02. A work presented a network that could classify different types of leaves while detecting diseases.
03. Some of them introduced an optimized DNN-based paddy leaf classification using the Jaya algorithm.
Methodology
Methodology
Tools used:
Workflow of the research:
Algorithms (Deep Learning)
CNN Algorithm
LSTM Algorithm
Bi-LSTM Algorithm
Data Preprocessing
Statistical Analysis
Dataset
Dataset
Result Analysis
Result Analysis
Bi-LSTM
Limitations
Limitations
Lack of real-time data basis on disease
The training data is in a limited amount
Understanding the model briefly as a user
Using the previous dataset as model
Conclusion
Conclusion
My research-based project, corn disease detection using deep learning holds great promise for enhancing crop management and reducing crop losses. With the help of neural networks and advanced image analysis techniques, deep learning models have the potential to accurately and efficiently identify and classify corn leaf diseases. However, challenges such as limited training data, interpretability issues, and sensitivity to image variations need to be addressed to ensure reliable and robust performance. Having these limitations, deep learning-based disease detection offers opportunities to improve early intervention, optimize resource allocation, and ultimately contribute to sustainable agriculture by protecting corn crops, ensuring food security, and supporting farmers in making informed decisions for disease management.
References
References
[1] Kusumo, B. S., Heryana, A., Mahendra, O., & Pardede, H. F. (2018, November). Machine learning-based for automatic detection of corn-plant diseases using image processing. In 2018 International conference on computer, control, informatics and its applications (IC3INA) (pp. 93-97). IEEE.
[2] Gavhale, K. R., & Gawande, U. (2014). An overview of the research on plant leaves disease detection using image processing techniques. Iosr journal of computer engineering (iosr-jce), 16(1), 10-16.
[3] Panigrahi, K. P., Sahoo, A. K., & Das, H. (2020, June). A cnn approach for corn leaves disease detection to support digital agricultural system. In 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) (pp. 678-683). IEEE.
[4] Zhu, J. H., Wu, A., & Li, P. (2012). Corn leaf diseases diagnostic techniques based on image recognition. In Communications and Information Processing: International Conference, ICCIP 2012 Aveiro, Portugal, March 7-11, 2012 Revised Selected Papers, Part I (pp. 334-341). Springer Berlin Heidelberg.
[5] Vishnoi, V. K., Kumar, K., & Kumar, B. (2021). Plant disease detection using computational intelligence and image processing. Journal of Plant Diseases and Protection, 128, 19-53.
[6] Mahalakshmi, S. D., & Vijayalakshmi, K. (2021). RETRACTED ARTICLE: Agro Suraksha: pest and disease detection for corn field using image analysis. Journal of Ambient Intelligence and Humanized Computing, 12(7), 7375-7389.
[7] Mishra, S., Sachan, R., & Rajpal, D. (2020). Deep convolutional neural network based detection system for real-time corn plant disease recognition. Procedia Computer Science, 167, 2003-2010.
[8] Mishra, S., Sachan, R., & Rajpal, D. (2020). Deep convolutional neural network based detection system for real-time corn plant disease recognition. Procedia Computer Science, 167, 2003-2010.
[9] Panigrahi, K. P., Das, H., Sahoo, A. K., & Moharana, S. C. (2020). Maize leaf disease detection and classification using machine learning algorithms. In Progress in Computing, Analytics and Networking: Proceedings of ICCAN 2019 (pp. 659-669). Springer Singapore.
[10] Sheikh, M. H., Mim, T. T., Reza, M. S., Rabby, A. S. A., & Hossain, S. A. (2019, July). Detection of maize and peach leaf diseases using image processing. In 2019 10th international conference on computing, communication and networking technologies (ICCCNT) (pp. 1-7). IEEE.
[11] Ashwini, C., & Sellam, V. (2022). Corn disease detection based on deep neural network for substantiating the Crop Yield. Appl Math, 16, 423-433.
[12] Deshapande, A. S., Giraddi, S. G., Karibasappa, K. G., & Desai, S. D. (2019). Fungal disease detection in maize leaves using haar wavelet features. In Information and Communication Technology for Intelligent Systems: Proceedings of ICTIS 2018, Volume 1 (pp. 275-286). Springer Singapore.
[13] Alehegn, E. (2019). Ethiopian maize diseases recognition and classification using support vector machine. International Journal of Computational Vision and Robotics, 9(1), 90-109.
[14] Yu, H., Liu, J., Chen, C., Heidari, A. A., Zhang, Q., Chen, H., ... & Turabieh, H. (2021). Corn leaf diseases diagnosis based on K-means clustering and deep learning. IEEE Access, 9, 143824-143835.
[15] Waheed, A., Goyal, M., Gupta, D., Khanna, A., Hassanien, A. E., & Pandey, H. M. (2020). An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Computers and Electronics in Agriculture, 175, 105456.
[16] Chen, J., Wang, W., Zhang, D., Zeb, A., & Nanehkaran, Y. A. (2021). Attention embedded lightweight network for maize disease recognition. Plant Pathology, 70(3), 630-642.
[17] Wang, N., Wang, K., Xie, R., Lai, J., Ming, B., & Li, S. (2009). Maize leaf disease identification based on fisher discrimination analysis. Scientia Agricultura Sinica, 42(11), 3836-3842.
[18] Chen, Y., Chen, X., Lin, J., Pan, R., Cao, T., Cai, J., ... & Zhang, X. (2022). DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification. Agriculture, 12(12), 2047.
[19] Shenbagam, P., & Sanjana, N. (2022, July). Corn Leaf Disease Detection; A Survey. In 2022 International Conference on Inventive Computation Technologies (ICICT) (pp. 1287-1294). IEEE.
[20] Zeng, W., Li, H., Hu, G., & Liang, D. (2022). Lightweight dense-scale network (LDSNet) for corn leaf disease identification. Computers and Electronics in Agriculture, 197, 106943.
[21] Akanksha, E., Sharma, N., & Gulati, K. (2021, January). OPNN: optimized probabilistic neural network based automatic detection of maize plant disease detection. In 2021 6th international conference on inventive computation technologies (ICICT) (pp. 1322-1328). IEEE.
[22] Liu, Z., Du, Z., Peng, Y., Tong, M., Liu, X., & Chen, W. (2020, June). Study on corn disease identification based on PCA and SVM. In 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) (Vol. 1, pp. 661-664). IEEE.
Page updated
Google Sites
Report abuse