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Contents
Contents
Introduction
Motivation
Objectives
Related Works
Methodology
Dataset
Result Analysis
Limitations
Conclusion
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. 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.
02. 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.
03. 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.
Methodology
Methodology
Algorithms
CNN Algorithm
LSTM Algorithm
Bi-LSTM Algorithm
Data Preprocessing
Statistical Analysis
Dataset
Dataset
Result Analysis
Result Analysis
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.
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