Unveiling the Potential: Machine Learning and Image Processing for Early Disease Detection in Tulsi Herb

Abstract

Tulsi (Ocimum tenuiflorum) herb is very much predisposed to infections that influence the growth of the plant and impact the farmer’s ability to learn about the environmental factors affecting the plant. To discover any type of plant infection at a very preliminary stage, a prediction model employing machine learning and image processing techniques can be developed to accelerate the method of disease detection and classification with high-performance metrics. The deployment of various variable preprocessing methods and distinctive factors in the feature extraction process appeared to enhance the implementation of infection recognition and categorization. This article intends to assess and explore the application and implementation of numerous approaches and developments regarding leaf infection categorization and classification. A thorough analysis is provided for disease infection and classification implementation upon examination of formerly recommended avant-garde methods. Finally, challenges and some commendations in this space are considered for the real-time implementation of numerous image computational algorithms for disease detection and recognition of Ocimum tenuiflorum.