Abstract: Plant diseases have been a source of concern for farmers and academics around the world from long time. It is critical to identify and manage these illnesses as soon as possible in order to avoid their spread and reduce their impact on crop output. The ability to analyse patterns and features from plant photos or data using Machine Learning algorithms allows us to diagnose diseases more quickly, accurately, and at scale. The paper presents a comprehensive review of machine learning techniques applied to plant disease prediction, emphasizing their effectiveness and limitations. An in-depth analysis on key factors such as the authors’ approaches, datasets employed, specific problem statements addressed, and performance metrics used to evaluate model effectiveness. Through the analysis, various critical research gaps in the existing literature has been identified. The findings includes the need for standardized datasets, the integration of real-time data collection methods and the integration of ml and deep learning techniques for predicting the plant disease. The study provides a structured framework for future research, guiding the development of more robust and scalable machine learning solutions in plant disease management.
Keywords: Plant Disease Prediction, Machine Learning, Predictive Models, ML Techniques, Disease Detection, Agricultural AI
|
DOI:
10.17148/IJARCCE.2024.13924
