Data Mining Techniques for Early Detection and Classification of Plant Diseases: An Optimization-Based Approach

Early diagnosis and efficient categorization of plant diseases are of utmost importance to support agricultural production and profitability. Besides, this research aims to investigate the feasibility that employs data mining framework together with optimization algorithms as approaches towards plant diseases identification and categorization systems. The model proposed uses the state-of-art algorithms including the decision trees, support vector machines and the deep learning techniques in the feature extraction and pattern recognition as well as binary classification. Furthermore, low-level optimization techniques like genetic algorithms as well as particle swarm optimization are used to fine tune the specific model parameters and to reduce the computational overhead for improving the detection efficacy still more. To make the training and testing more reliable the system has incorporated a large dataset of diverse plant disease images along with the different environmental factors. The developed experiments show increased performance in terms of the identification of diseases and classification for the extraction of features compared to conventional methodologies. Together, the results highlight the possibility of applying data mining and optimization approaches for creating UT&C-based, translatable and sustainable plant disease management solutions at low costs of investment. This research has found its applicability in the field of precision agriculture as it offers suggestions and a technological solution to optimize the yield in the face of plant diseases that threaten world food security. A future work will be about implementation of real-time data acquisition and integration of highly sophisticated sensor technology for better responsiveness of the system at least to certain extent.