Progresses in Sugarcane Leaf Defect Identification : A Review

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Available Online at : www.ijsrcseit.com doi : https://doi.org/10.32628/CSEIT2410581 ISSN : 2456-3307 Progresses in Sugarcane Leaf Defect Identification : A Review Dr. Sheshang Degadwala1*, Dhrumil Dave2 1* Professor & Head, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India Research Scholar, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India 2 ARTICLEINFO ABSTRACT Sugarcane is a crucial crop for the global sugar industry, but its yield and quality Article History: Accepted : 20 Aug 2024 Published: 05 Sep 2024 can be significantly impacted by various leaf defects. Accurate and timely identification of these defects is essential for effective pest management and crop improvement. This review paper explores recent advancements in sugarcane leaf defect identification, focusing on technological progress and methodological innovations. The study covers traditional techniques, such as visual inspections Publication Issue and manual identification, and examines how modern approaches, including Volume 10, Issue 5 machine learning, computer vision, and remote sensing, have transformed the Sep-Oct-2024 field. Recent progress in image processing technologies and the development of automated systems have greatly enhanced the accuracy and efficiency of defect Page Number detection. Despite these advancements, challenges remain, including variability 01-11 in defect appearance, the need for large annotated datasets, and the integration of detection systems into practical agricultural practices. The review also discusses the impact of these technologies on improving disease management, optimizing yield, and supporting sustainable farming practices. By highlighting current trends and future directions, this paper aims to provide a comprehensive understanding of the state-of-the-art methods in sugarcane leaf defect identification and their implications for the agricultural industry. Keywords: Sugarcane, Leaf Defect Identification, Image Processing, Machine Learning, Computer Vision, Remote Sensing, Pest Management. I. INTRODUCTION sugarcane can be severely compromised by various leaf defects, which are often indicative of underlying issues Sugarcane (Saccharum officinarum) is a vital crop such as pest infestations, nutrient deficiencies, or globally, serving as a primary source of sugar and biofuel. diseases. Early and accurate identification of these Its cultivation is essential for the agricultural economy, defects particularly in tropical and subtropical regions where it management strategies and ensuring optimal crop yield. thrives. However, the productivity and quality of Traditionally, identifying leaf defects has relied on is crucial for implementing Copyright © 2024 The Author(s) : This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) effective 1 Dr. Sheshang Degadwala et al Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., September-October-2024, 10 (5) : 01-11 manual inspections by field experts, a labor-intensive on crop health and defect distribution. This approach is process that is both time-consuming and prone to particularly beneficial for large-scale operations where human error. As a result, there has been a growing manual inspection is impractical. By integrating remote interest advanced sensing data with machine learning models, researchers technologies to enhance the accuracy and efficiency of can enhance the accuracy of defect detection and gain defect identification. insights into spatial patterns and environmental factors in developing and applying influencing defect occurrence. Despite these technological advancements, several challenges remain in the field of sugarcane leaf defect identification. One of the primary challenges is the variability in defect appearance due to different causes, such as pest species, disease types, and environmental conditions. This variability can complicate the development of generalized detection models and requires the creation of extensive and diverse datasets Figure 1: Example of Sugarcane Leaf Defect [1] Recent advancements in image processing, machine learning, and computer vision have revolutionized the field of agricultural diagnostics, offering new opportunities for automating and improving the detection of leaf defects. Image processing techniques, such as segmentation and feature extraction, allow for the detailed analysis of leaf images to identify and classify defects. These methods can analyze visual data with high precision, detecting subtle variations in leaf appearance that might be missed during manual inspections. Machine learning algorithms, particularly deep learning models, have further advanced the capability to recognize patterns and anomalies in large datasets, providing robust and scalable solutions for defect identification. Computer vision systems equipped with high-resolution cameras and sophisticated algorithms can process real-time data, enabling timely interventions and minimizing crop damage. Remote sensing technologies have also contributed significantly to the progress in defect identification. Through aerial imagery and satellite data, remote sensing allows for the monitoring of large sugarcane fields from a distance, providing valuable information for training machine learning algorithms. Additionally, the integration of these technologies into practical agricultural practices presents hurdles, including the need for user-friendly systems and the adaptation of existing farming practices to incorporate automated detection methods. The impact of advancements in defect identification technologies management. extends beyond Enhanced improving detection crop capabilities contribute to better disease management, optimized fertilizer and pesticide use, and increased overall yield and quality of sugarcane. Furthermore, the application of these technologies supports sustainable agricultural practices by reducing the reliance on manual labor and minimizing the use of chemicals, thus promoting environmental sustainability. This review aims to provide a comprehensive overview of the progress made in sugarcane leaf defect identification, highlighting advancements, current key technological methodologies, and the challenges that remain. By examining the state-of-theart techniques and their implications for the sugarcane industry, the review seeks to offer valuable insights into the future directions of research and development in this critical area of agricultural science. Volume 10, Issue 5, September-October-2024 | http://ijsrcseit.com 2 Dr. Sheshang Degadwala et al Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., September-October-2024, 10 (5) : 01-11 II. LITERATURE STUDY TABLE I COMPARATIVE ANALYSIS No. 1 Title Publication Year Enhanced Deep Limitation/Future Algorithms Used 2024 Enhanced Work Deep Integration with real- Learning Technique for Learning Technique, time Sugarcane Leaf Disease Mobile Application systems; Classification Integration Mobile and monitoring improve accuracy and efficiency Application Integration 2 Deep Learning Approaches for 2024 the Deep Learning Techniques Explore more diverse datasets; enhance model Detection, robustness Classification, and Analysis of Sugarcane Leaf Disease 3 Sugarcane Health Monitoring Satellite 2024 With Satellite Need for more field Spectroscopy, Machine experiments; integrate Spectroscopy Learning more and Machine Learning: advanced ML techniques A Review 4 Sugarcane Leaf 2024 Dataset: A Dataset for Data Collection, Dataset Creation Extend dataset to include more disease Disease Detection and types; improve dataset Classification diversity Machine for Learning Applications 5 Genome-Wide Identification Characterization 2024 and of Genome-Wide Focus Identification, Characterization on other transcription factors; of explore potential Homeobox Homeobox treatments Transcription Factors in Transcription Factors Phoma Sorghina Var. Saccharum Causing Sugarcane Twisted Leaf Disease 6 Plant Detection Disease and Classification Techniques: 2024 Comparative Various Detailed performance Classification metrics Techniques A Study, evaluate needed; more classification algorithms Volume 10, Issue 5, September-October-2024 | http://ijsrcseit.com 3 Dr. Sheshang Degadwala et al Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., September-October-2024, 10 (5) : 01-11 Comparative Study of the Performances 7 Artificial Intelligence 2024 Artificial Intelligence Framework for Multi- Framework, Class Sugarcane Leaf Class Classification Real-time Multi- implementation; extend to other plant diseases Diseases Classification 8 Technological 2024 Technological Innovation in Disease Innovation, Detection Management Detection Management and in Long-term field tests; Disease integration with smart and agriculture technologies Sugarcane Planting 9 Classification of 2024 AlexNet Model, Compare with other Sugarcane Leaf Disease Classification deep learning models; with AlexNet Model Techniques optimize for computational efficiency 10 A Deep Learning- Based Approach for Early Detection of 2024 Deep Learning, Explainable AI Focus on explainability; validate with larger datasets Disease in Sugarcane Plants: An Explainable Artificial Intelligence Model 11 Image Classification of Sugarcane Density Aphid Using Convolutional 2023 Deep Convolutional Neural Deep Broaden scope to Networks, other pests; optimize Image Classification Neural model for real-time usage Networks 12 SLViT: Shuffle- 2023 Lightweight Vision Convolution-Based Transformer, Lightweight Vision Convolution-Based Transformer for Test on diverse Shuffle- datasets; improve model interpretability Approach Effective Diagnosis of Sugarcane Leaf Diseases 13 SE- 2023 VisionTransformer: Hybrid Network Diagnosing Hybrid Vision for Network, Explore more hybrid Transformer, architectures; Attention Mechanism validate with real-world data Sugarcane Leaf Diseases Based on Attention Mechanism 14 Detection and Identification of Healthy and Unhealthy 2023 Convolution Neural Network Improve detection System, speed; expand to detect more types of diseases Volume 10, Issue 5, September-October-2024 | http://ijsrcseit.com 4 Dr. Sheshang Degadwala et al Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., September-October-2024, 10 (5) : 01-11 Sugarcane Leaf Using Detection Convolution Identification Neural and Network System 15 Recognition of 2023 Leaf Sugarcane Orange and Processing, Brown Rust through Recognition Leaf Image Processing Image Enhance image Disease processing techniques; apply to more diseases and larger scale studies III.TENTATIVE SYSTEM METHODOLOGY 4. CNN with Attenuation Model Training: In this step, the CNN model, with an attenuation mechanism (possibly for enhancing the feature extraction process), is trained using the preprocessed data. The attenuation component likely helps the model focus on more relevant features for accurate disease detection. 5. Trained Model: Once the CNN has been trained, the resulting trained model is ready for deployment. 6. Classification: Using the trained model, the system classifies the input test images into various disease categories. 7. Disease Output: The final block lists the Figure 2: Tentative System Methodology diseases that the model is capable of identifying Smut, Yellow Leaf Disease, Pokkah Boeng, The figure 2 depicts a flowchart outlining the steps of a Mosaie, Grassy Shoot, Brown Spot, Brown Rust, convolutional neural network (CNN) model for disease classification in plants, specifically for sugarcane Banded Chlorosis, and Sett Rot. Overall, this flowchart represents a CNN-based diseases: machine learning pipeline aimed at diagnosing 1. Start: The process begins at the "Start" block, sugarcane leaf diseases from images. initiating the overall procedure for disease detection using a CNN. IV.CONCLUSION AND FUTURE WORK 2. Dataset Reading: This step involves loading the dataset, which contains images of sugarcane leaves showing various diseases. 3. Pre-processing: Balancing, made in sugarcane leaf defect identification through Resize, the application of image processing, machine learning, Normalization: Before the CNN can train and remote sensing technologies, existing models still effectively, the data is pre-processed. This face challenges in robustness and accuracy. Current includes balancing the dataset to ensure the machine learning and transfer learning models often model is not biased towards certain disease struggle with variability in defect appearance and types. The images are resized to a standard size, environmental conditions, leading to suboptimal and normalization is applied to bring pixel performance. To address these limitations, developing values to a common scale, optimizing the a custom Convolutional Neural Network (CNN) with attenuation modeling presents a promising approach. training process. Data In conclusion, while significant progress has been Volume 10, Issue 5, September-October-2024 | http://ijsrcseit.com 5 Dr. Sheshang Degadwala et al Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., September-October-2024, 10 (5) : 01-11 This model aims to enhance detection precision by better accounting for variations in [5] defect Bao, Yixue, et al. “Genome-Wide Identification and Characterization of Homeobox characteristics and environmental factors, potentially Transcription Factors in Phoma Sorghina Var. leading to more reliable and consistent results in real- Saccharum Causing Sugarcane Twisted Leaf world conditions. Disease.” International Journal of Molecular Sciences, Future work will focus on refining this custom CNN to improve its accuracy and robustness. This involves vol. 25, no. 10, 2024, https://doi.org/10.3390/ijms25105346. [6] Demilie, Wubetu Barud. “Plant Disease fine-tuning the model, expanding the training dataset, Detection and Classification Techniques: A and validating its effectiveness across diverse field Comparative Study of the Performances.” conditions. Additionally, integrating the CNN with remote sensing technologies and automated Journal of Big Data, vol. 11, no. 1, 2024, https://doi.org/10.1186/s40537-023-00863-9. monitoring systems could offer a more comprehensive [7] Technology, Applied Information, et al. and “Artificial Intelligence Framework for Multi- management. By advancing these technologies, the Class Sugarcane Leaf Diseases Classification.” goal is to provide farmers with a practical and accurate Journal of Theoretical and Applied Information tool for managing sugarcane crop health, thereby Technology, vol. 102, no. 10, 2024, pp. 5277–90. solution for real-time defect detection improving yield, optimizing resource use, and [8] supporting sustainable agricultural practices. 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