Transparent Peer Review By Scholar9
RICE PLANT LEAF DISEASE DETECTION USING AI
Abstract
Agriculture is an ultimate necessity on the same note it is main source that offers domestic income to many countries around the world. Diseases affecting plants from different pathogens such as viruses, fungi or bacteria are costly to agriculture around the world in terms of losses as indicated below. In the same regard we consider applications from a genomics physiology biochemistry perspective among others. Amongst all the crops that are cultivated in India the rice crop is said to be a major crop that is vulnerable to several diseases in its growth cycle at one time or another. Manual diagnosis of these diseases by farmers is not easy because they do not have the capacity to diagnose them without training. This is why disease identification and treatment of the infected specimens is imperative in order to get to a normal and healthy point of rice plants. In the modern world, disease detection especially on the leaves is very crucial in today’s topic of agriculture. Our algorithm also has the ability to diagnose diseases on rice leaves. Our goal in this study will be to perform classification of disease images in rice leaves with complex backgrounds and different lighting conditions. Using the CNNs based model on the data set acquired from Kaggle, it gives us the accuracy level of 98%. The results of disease identification in rice indicate how useful the proposed method is. Diagnosis of diseases, CNN algorithm, rice leaf, and machine learning are keywords. Rice diseases automatic detection and analysis are needed by the farming industry in order to minimize the wastage of the financial and other valuable resources, reduction of yield loss, increase processing efficiency and attainment of healthy crop yield.
Shyamakrishna Siddharth Chamarthy Reviewer
10 Oct 2024 06:24 PM
Approved
Relevance and Originality
The research addresses a significant issue in agriculture, particularly in India, where rice is a major crop susceptible to various diseases. The focus on automated disease detection is highly relevant, given the increasing demand for efficient agricultural practices and food security. The originality of the study lies in its application of advanced machine learning techniques, specifically Convolutional Neural Networks (CNNs), to classify disease images in rice leaves. This approach not only contributes to existing knowledge in agricultural technology but also offers practical solutions for farmers, thereby enhancing its significance in real-world applications.
Methodology
The study employs a CNN-based model to classify rice leaf disease images, leveraging a dataset acquired from Kaggle. The methodology appears robust, involving the classification of images with varying backgrounds and lighting conditions, which is crucial for real-world applicability. However, the description of the algorithmic approach could benefit from more detail, including the specific architecture of the CNN used, preprocessing steps applied to the images, and any data augmentation techniques implemented to improve model performance. Additionally, clarifying the evaluation metrics employed would enhance the transparency of the methodology.
Validity & Reliability
The reported accuracy level of 98% for disease detection using the CNN model suggests a high degree of effectiveness. This implies that the model has been validated through appropriate testing, likely using a well-curated dataset. However, to establish the reliability of the findings, the article should provide more information about the dataset's size, diversity, and how the training and testing datasets were split. Addressing potential biases in the dataset and discussing the model's performance on unseen data would strengthen the reliability of the conclusions drawn.
Clarity and Structure
The article is generally well-structured, presenting a clear progression from the introduction of the problem to the methodology and findings. The language is straightforward, making the technical content accessible to a broader audience. However, the inclusion of visual aids, such as flowcharts, diagrams, or example images of affected rice leaves, could enhance clarity and engagement. Additionally, defining key terms and concepts related to machine learning and disease detection would benefit readers who may not be familiar with these topics.
Result Analysis
The analysis of results indicates a strong performance of the proposed CNN model in accurately identifying rice leaf diseases. Highlighting the practical implications of achieving such high accuracy is essential, as it underscores the potential for this technology to assist farmers in disease management, thereby improving crop yield and reducing losses. However, the result analysis could be further enriched by discussing the clinical relevance of the findings, comparing them with existing detection methods, and exploring the implications for future research and development in automated disease detection in agriculture. Additionally, providing insights into how this approach could be scaled for widespread adoption in farming practices would add value to the discussion.
IJ Publication Publisher
Ok Sir
Shyamakrishna Siddharth Chamarthy Reviewer