Open AccessRice Plant Disease Detection Using Sensing Recognition Strategy Based on Artificial Intelligence
Author(s) -
T. Daniya,
Ch. Vidyadhari,
Srilakshmi Aluri
Publication year - 2022
Publication title -
journal of mobile multimedia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.229
H-Index - 12
eISSN - 1550-4654
pISSN - 1550-4646
DOI - 10.13052/jmm1550-4646.18311
Subject(s) - rice plant , identification (biology) , schedule , paddy field , position (finance) , computer science , agricultural engineering , business , microbiology and biotechnology , agronomy , engineering , biology , ecology , finance , operating system
In current history rice infections have often appeared, causing severe destruction of rice cultivation. As one of the top ten countries that creates and destroys the world, India relies heavily on rice for its economy and to meet its food needs. To ensure the sound and legal growth of rice crops it is important to identify any diseases in the schedule and to pre-apply the expected treatment to the affected plants. Since the detection of disease is time-consuming and labor-intensive, it is certainly wise to have a system with robots. Infection of rice crops is considered to be a growing factor behind the horticultural, financial and general situation in the future development of the rural field. However, leaf scald and eyespot are the pivotal trouble in paddy fields. Hence, to conquer these issues a novel Sensing Recognition Strategy has been proposed. In Proposed method, optical sensors identify identification of disease and Enhanced Grasshopper Detection Algorithm utilizing the grasshoppers’ forces, path and position carries out detection. The accuracy of the suggested framework is to attain 97.94% with healthy rice crops.