An Optimized Machine Learning Model for Tomato Leaf and Fruit Disease Detection using Kernel Extreme Learning Machine (KELM) Model with Firefly Optimization
Keywords:
Tomato, Disease Detection, Firefly optimization, Kernel extreme learning machineAbstract
Tomato, botanically called Solanum Lycopersicum is a profoundly cultivated cash crop. It is a very common plant which has wide usage across the globe because of its pharmacological properties. Tomato plants are prone to many diseases triggered by various organisms like virus, fungus, bacteria, nematodes, and sometimes environmental conditions also. It is impossible for the farmer to visually identify them. Hence, this research work presents a classification system that automatically recognizes diseases of tomato leaves and fruits. In this study, an optimized machine learning classifier is proposed to classify disease types in tomato leaves and vegetables. The presented technique uses adaptive histogram equalization Contrast Limited AHE (CLAHE) to enhance the input images. In addition, the two-level nested U-structure architecture is employed for segmentation so that the affected diseased portions are identified in the pre-processed images. Besides, LBP, Gary-Level Co-occurrence Matrix (GLCM) are utilized for feature extraction process. For disease detection and classification, the proposed model uses kernel extreme learning machine (KELM) model with firefly optimization (FFO) based parameter optimizer. The performance validation is done using dataset from Haggle repository and our own dataset. The proposed model leverages the power of machine learning algorithms to automatically analyse and classify diseases based on digital images of tomato leaves and fruits.
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