An effective IoT-assisted adaptive thresholding and hybrid deep learning model for rice blast fungal detection

Abstract

ABSTRACT In Asian countries, rice is the major agricultural product. Hence, the initial identification of plant disorder via the IoT (Internet of Things) offers to ignore the rice from critical disorders. To increase crop production, the measurements should be considered to completely destroy the rice plant disorders by an effective system. This paper proposed an Intelligent IoT-aided deep learning model for detecting rice blast fungal along with a hybrid heuristic algorithm. The proposed work encompasses with multiple stages that are explained as follows. At first, the required paddy images are gathered from online data resources. Next, the pre-processing of the collected images is made by adaptive mean filtering and contrast enhancement. Further, the adaptive thresholding and morphological operation are adopted for leaf segmentation purposes, where the threshold value is tuned by a Fitness-based Billiards-inspired Rat Swarm Optimizer (FBRSO). Consequently, from the segmented image, the Region of Interest (ROI) is cropped. Finally, the cropped ROI is subjected to the Optimized MobileNetv2 and Multiscale Residual Attention Network (OMMRAN), where it includes MobileNetV2 and Multiscale Residual Attention Network, in which some of the hyperparameters are tuned by FBRSO approach. The performance is validated and compared with other existing approaches for detecting rice diseases effectively.