RBD-AIIoT: Rice Blasts Detection Combining AI & IoT

Abstract

Rice blast disease is the most common disease in rice-growing areas in the world, and it is the most serious in India. Rice is threatened by a number of illnesses. For precise disease prevention and control, it is critical to establish rapid and accurate identification of rice plant illnesses. Many rice explosion organization methods necessitate the expertise of experienced agriculturalists. Keeping a watch on the farm for signs and symptoms of contamination takes a lot of time and work. To detect rice plant illnesses, current techniques investigation use snap shots or non-photo hyper spectral statistics, which necessitate human strategies to obtain the snap photos or data for evaluation. Rice blast detection is based on a non-photograph IoT sensor-based IoT infrastructure for soil cultivation. Unlike image-based fully plant disease detection systems, our agricultural sensors generate quasi information that can be routinely taught and analyzed by the AI mechanism in real time. This research proposes RBD-AIIoT method for detecting rice plant ailments that combines AI and IoT tools. RBD-AIIoT provides agriculture sensors generate non-image facts that AI can robotically analyses and study, unlike photo-based solutions for plant disease forecasting and also this proposed system sense Temperature, Humanity, Soil, Rain and Pressure of the environment to detect the rice blasts.