Whale Optimization based Deep Residual Learning Network for Early Rice Disease Prediction in IoT

Abstract

Disease detection on a farm requires laborious and time-consuming observation of individual plants, which is made more difficult when the farm is large and many different plants are farmed. To address these problems, cutting-edge technologies, AI, and Deep Learning (DL) are employed to provide more accurate illness predictions. When it comes to smart farming and precision agriculture, IoT opens up exciting new possibilities. To a certain extent, the goal-mouth of "smart farming" is to upsurge productivity and efficiency in agricultural processes. Smart farming is an approach to agriculture in which Internet of Things devices are interconnected and new technologies are used to optimize existing methods. Utilizing Internet of Things (IoT) devices, smart farming aids in more informed decision making. In many parts of the world, rice is the staple diet. This means that early detection of rice plant diseases using automated techniques and IoT devices is essential. Growing rice yields and profits may be helped along by DL model creation and deployment in agriculture. Here we introduce DRL, a deep residual learning framework that has been trained using photos of rice leaves to recognize one of four classes. The suggested model is called WO-DRL, and the hyper-parameter tuning procedure of DRL is executed with the help of the Whale Optimization algorithm. The outcomes demonstrate the efficacy of our suggested approach in directing the WO-DRL model to learn important characteristics. The findings of this study will pave the way for the agriculture sector to more quickly diagnose and treat plant diseases using AI.