What is it about?
Plant diseases constitute a significant risk to both the quantity of food that may be produced by cultivation and the availability of food on a global scale. In order to effectively control leaf diseases, it is absolutely necessary to perform procedures for early detection and accurate classification. The methods of machine learning (ML) have experienced rapid development in recent years, making them ideal for the automated detection and classification of plant diseases. This was made possible by the rapid development of ML. The purpose of this review paper is to provide an overview of the developments that have taken place regarding the application of ML in the identification and categorization of plant leaf diseases. Methodologies, data sets, and metrics for evaluating results are all covered in this discussion as separate topics. In addition to this, it discusses the potential of machine learning as well as the areas of management of plant diseases in which it could potentially be used in the future. The findings of this review emphasize the value of machine learning-based systems in assisting farmers, researchers, and other stakeholders in lessening the detrimental effects that plant diseases have on crop yields. This review was conducted by the National Center for Biotechnology Information.
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Why is it important?
Machine learning-based leaf disease detection and classification in plants is important because it enables early, accurate, and automated identification of plant diseases, which is crucial for ensuring high crop yield and food security. Traditional methods are time-consuming, labor-intensive, and often prone to human error, whereas machine learning models can analyze vast image datasets quickly and consistently. These systems help farmers make timely decisions for disease management, reducing the need for excessive pesticide use and lowering costs. Moreover, they support precision agriculture by offering scalable and real-time monitoring solutions. This technological advancement is particularly vital in addressing the challenges posed by climate change and increasing global food demands.
Perspectives
The perspective on machine learning-based leaf disease detection in plants highlights its potential to transform traditional agricultural practices. By leveraging image processing and predictive algorithms, it offers a proactive approach to plant health management. This not only improves diagnostic accuracy but also empowers farmers with accessible and efficient tools. As agriculture shifts toward digitization, such technology-driven solutions become essential for sustainable farming. The integration of machine learning ensures scalability and adaptability across various crops and environments.
Om prakash Suthar
Marwadi Education Foundation Trust
Read the Original
This page is a summary of: Machine learning-based leaf disease detection and classification in plants-A review, January 2025, American Institute of Physics,
DOI: 10.1063/5.0240221.
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