Authors: Mr. Kumar K, N Keerthana, Sahana R, Shilpa M, Vandana N
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Sugarcane is a vital crop, and its health directly impacts agricultural yields and the sugar industry. To address the challenges associated with disease detection in sugarcane, we propose an approach using deep learning techniques. Our study leverages convolutional neural networks (CNNs) and image analysis to accurately identify and classify various sugarcane diseases. By analyzing high-resolution images of sugarcane leaves and stems, our deep learning algorithm provides remarkable accuracy in disease detection, offering a promising solution for early diagnosis. This research contributes to sustainable agriculture and aids in preserving the economic viability of sugarcane cultivation.
Sugarcane is a vital crop that is essential to the global agriculture industry, serving as a primary source of sugar production and bioenergy. However, the cultivation of sugarcane is constantly challenged by the appearance of numerous diseases that can significantly reduce yield and quality. Timely and accurate disease detection is necessary for sustainable sugarcane farming and efficient disease control. Traditional methods of disease diagnosis in sugarcane rely on visual inspection by experts, which may require a lot of time, subjective and prone to errors. To address these challenges, the integration of deep learning algorithms has emerged as a promising solution for automating and increasing the accuracy of sugarcane disease detection.
In recent years, deep learning has revolutionized the field of computer vision, enabling machines to automatically learn and identify complex patterns and features within images. This technology has found applications in various domains, including agriculture, where it has the potential to enhance crop disease detection. Deep learning algorithms can be used to build reliable and effective systems for the automatic diagnosis of sugarcane diseases, thus reducing the reliance on manual inspection and improving the overall health and productivity of sugarcane crops. In this paper, we delve into the exciting field of sugarcane disease detection using deep learning. We explore the challenges associated with traditional disease diagnosis methods, highlight the advantages of deep learning, and present recent developments in the application of this technology for identifying sugarcane diseases. Furthermore, we discuss the potential impact of this innovative approach on the sugarcane industry, emphasizing the benefits of early disease detection, improved resource management, and enhanced crop sustainability. By leveraging the capabilities of deep learning, we aim to pave the way for a more efficient, accurate and sustainable future in sugarcane cultivation.
II. PROBLEM STATEMENT
The cultivation of sugarcane plays a pivotal role in the global sugar industry, contributing significantly to the world's sugar supply. However, the occurrence of various diseases in sugarcane plants poses a significant threat to crop yield and quality. Detecting these diseases early is crucial for effective disease management, but traditional methods are often labor-intensive and time-consuming. This project aims to deal with the issue by developing a deep learning-based solution for the automated and accurate detection of sugarcane diseases. The challenge is to produce a robust and effective model that can categorize and identify these diseases in real-time, aiding farmers in timely intervention and minimizing crop losses.
III. EXISTING SYSTEM
The Sugarcane Disease Detection system makes use of machine learning methods to identify and classify diseases affecting sugarcane crops. It relies on image recognition algorithms that analyze images of sugarcane leaves to identify common diseases like rust, smut, and mosaic virus. The system employs a dataset of labeled images for training and validation, and it continuously learns and updates its disease detection models. This technology aids farmers to detect diseases in advance, allowing for timely intervention and improved crop yield. It contributes to sustainable agriculture by reducing the usage of pesticides and enhancing crop management practices.
IV. RELATED WORKS
Sugarcane Disease Detection Using CNN Deep Learning: An Indian Perspective - In the paper , the design uses a simplified convolutional neural network with four different classes to detect sugarcane diseases with 98.69% accuracy. The trained model was successful in recognizing and classifying sugarcane images into diseased/diseased and non-diseased/healthy groups based on sugarcane leaf pattern and disease. Thanks to this research, farmers can identify and classify sugarcane diseases using computer vision and machine learning. The main objective of the research presented here is to provide inputs to CNN, which would provide essential assistance to farmers through a web-based application that can be accessed from any web-connected device (e.g., desktop, mobile, tab, etc.) needs. A trained model on real-time images can be well used in detecting and identifying plant diseases. The proposed system is a web application. Thus, we can log into the system and easily enter the image into the classifier to get a result that makes the system more user-friendly. The usefulness and application of this technique depends on how well the model can be adapted to real-world use through a web-based application. Customization based on user feedback of the model can be considered as a future area of this research. An adaptive and continuously updated robust dataset based on user input is the key to the utility and application improvement of this technology. This can be considered as a limitation of the study and can be considered as a future area for other researchers along with updating the dataset in an iterative mode while the web-based application is running.
The developed model was capable to identify the presence of leaves and differentiate between healthy and unhealthy leaves that can be diagnosed visually. Various experiments were carried out to verify the conduct of the new model. A new plant disease dataset was created, containing certain number of images taken from available online sources and expanded to even more number of images with suitable variations. Improvements did not show significant changes in overall accuracy, but the addition process had a greater impact on achieving respectable results. Since the presented method cannot be used in the field of detection of plant diseases, no comparison was made with similar results using precise technology. An extended version of this research concerns image collection to enhance the dataset and improve model accuracy using various fine-tuning and upscaling techniques. This program helps agriculturists to quickly and efficiently recognize plant diseases and facilitate making decisions while using chemical pesticides. In addition, future enhancements will expand the usage of the trained model to recognize plant diseases over larger land. By expanding this analysis, the authors desire to make a helpful significance on sustainability by influencing the quality of crops for generations to come.
A. Flow Diagram
2. Data Preprocessing:
3. Image Segmentation:
4. Feature Extraction:
5. Deep Learning Model:
6. Classification of Disease
7. Result Analysis:
We would like to express our deep gratitude to Mr. KUMAR K for his valuable and constructive suggestions during the planning and development of this project. His willingness to give his time so generously has been very much appreciated. We would also like to thank all the professors of KSIT for their continuous support and encouragement.
In conclusion, the application of deep learning in sugar cane disease detection represents a groundbreaking advancement in agriculture and crop management. The use of neural networks and image analysis has demonstrated remarkable accuracy in identifying and classifying diseases affecting sugar cane crops. This technology promises to revolutionize the way farmers monitor and protect their fields, enabling early detection, targeted intervention, and the potential for increased yields and sustainability. The benefits extend beyond individual farmers to the broader agricultural community and food security. However, continued research and refinement are essential to harness the full potential of this technology, ensuring its successful integration into agricultural practices. Ultimately, the convergence of deep learning and agriculture holds great promise for more efficient and sustainable crop production in a rapidly evolving world.
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Copyright © 2023 Mr. Kumar K, N Keerthana, Sahana R, Shilpa M, Vandana N. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.