An Optimal Classification Model for Rice Plant Disease Detection

: Internet of Things (IoT) paves a new direction in the domain of smart farmingand precision agriculture. Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent. In smart farming, IoT devices are linked among one another with new technologies to improve the agricultural practices. Smart farmingmakes use of IoT devices and contributes in effective decision making. Rice is the major food source in most of the countries. So, it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT devices. The development and application of Deep Learning (DL) models in agriculture offers a way for early detection of rice diseases and increase the yield and profit. This study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted ExtremeLearning Machine (CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart effectively diagnosed the disease with high sensitivity of 0.905, specificity of 0.961, and accuracy of 0.942.

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