Optimized data mining algorithms for enhancing accuracy in plant disease detection and management system

Algorithms have been improved by the modern data mining techniques in the detection of accurate diagnosis of plant diseases and management system like never before. In particular, the study aims at collecting as many methods as possible on how to enhance the process of data mining to enhance the accuracy of plant disease diagnosis based on various data sources, i.e. sensors, image processing and other environmental data. This research paper is based on machine learning, deep learning, and hybrid methods to outline an integrated solution to developing an efficient and effective system to diagnose plant diseases that would help prevent losses that could otherwise be disastrous in the future. These streamlined algorithms will be dealing with possibly huge datasets, discover patterns of significance, and diagnose diseases at the earliest, as a way of offering likely solutions to sustainable agriculture issues. Key aspects of the proposed framework include feature extraction, classification and model fine-tuning, which together enhance the overall predictive performance and accuracy of the disease diagnosis system.