Tulsi Herb s Infection Classification And Prediction Using Artificial Intelligence

Abstract

This thesis investigates the development and implementation of a disease prediction model for newlineTulsi flowers (Ocimum sanctum). The broad-spectrum purpose of this study is to meet the newlinepressing need for early detection of disorders in Tulsi farming to reduce production losses and newlineensure long-term crop health management. With modern technologies such as computer vision newlineand machine learning, this research focuses on automating disease prediction using leaf images newlineas primary diagnostic information. Different image processing techniques, feature extraction newlinemethods and predictive approaches were investigated with a view to improve the accuracy and newlineefficiency of disease prognosis. Through a systematic approach, the team came up with a robust newlineprediction model that could diagnose some common diseases affecting Tulsi plants based on newlinevisual symptoms observed from images taken on leaves. Designed models are expected to newlineassist better precision agriculture practices as well as plant health monitoring strategies which newlineoffer bright prospects for researchers, practitioners, stakeholders involved in Basil cultivation newlineor agricultural production. The aim of this research is to develop an automated system for newlinedetecting tulsi diseases using leaf images. newline