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The detection of different types of diseases in leaves is an important technique for enhanced food production. Every feasible method for increasing the supply of food helps to reduce the problems of a leaf's disease. This paper proposes a new ensemble learning model with the integration of soft computing and deep learning approaches for the multi-disease classification of plant leaves. The architecture of ensemble learning is modified by Oppositional Adaptive Galactic Swarm Optimization (OA-GSO) for tuning the parameters of CNN, SVM, and DNN for getting superior classified outcomes. From the result analysis, the accuracy of the OA-GSO-Ensemble is superior in comparison with 6.66% for DT, 4.34% for SVM, 6.66% for NN, and 9.09% for the Ensemble model, and 5.49% for IDNN respectively at varying learning percentage of 35. Thus, the experimental result demonstrated that the suggested model achieves high accuracy while evaluating other conventional models.

Soft computing-based ensemble learning model for multi-disease classification of plant leaves