Tuned weighted feature fusion with hybridized DNN-RNN framework for plant disease detection and classification

In the world economy, agriculture is an essential part among individuals to earn money. But, the farmers face more obstacles because more diseases naturally affect the health of the plant. Hence, to overcome this limitation, automatic plant disease identification techniques are essential to monitor the growth as well as disease-affected plants and leaves. In most plants, symptoms related to plant disease can be identified by considering the leaves. Moreover, plant disease identification became more essential with deep learning architecture. Hence, it is important to enhance the solution for resource-constrained portable devices like smartphones, but it become a complicated issue as the deeply structured methods utilize extensive resources for the analysis. Hence, this paper aims to develop an automated plant disease detection framework with an advanced set of plant features. The plant images are taken from online sources. The acquired plant images undergo image enhancement using the contrast enhancement process. The enhanced images are fed in the feature retrieval phase 1, where the segmentation takes place using the Centre Optimized K-means clustering (CO-KMC) with the Modified Random Variable-based Black Widow Optimization (MRV-BWO). Later, the segmented images obtain the colour, shape, and texture features and they are considered as the first set of features. The second set of features is retrieved from the enhanced images using Multi-scale Dilated Attention CNN (MSDA-CNN). These two features are fused by the weighted feature fusion process using the developed MRV-BWO. Then, the plant diseases are detected using the Hybridized Deep Neural Network with Recurrent Neural Network (HDNN-RNN), which comprises RNN and DNN models. The parameter optimization takes place in the hybrid HDNN-RNN for improving the detection efficiency using MRV-BWO. From the verification of the findings, the recommended model obtains a 96.47 precision and accuracy rate. Therefore, the simulation results of the offered model reveal that it achieves enriched efficacy when comparing other baseline approaches.