Rice Insect Classification and Quantification (RICQ) Using Portable Neural Network Model with EFPV-Image Processing Algorithm

Allan C. Taracatac and Ricardo Q. Camungao

Countries like the Philippines are considered as Malthusian areas, where the very rapid rate of growth of the population tends to outrun the capacity to produce food. More than 90% of the world’s rice is grown and consumed in Asia where 60% of the earths’ people live. Insect pests caused maximum loss towards seed yield by 27.9%. With the system prototype, an impressive classification accuracy rate of 99% to Cutworm, grasshopper, locust, and armyworms. Detection and quantification of rice pest insects provide clearer visualization of the actual insect manifestation on rice fields. Thus, it provides decision support to rice farmers for necessary intervention.

Volume 11 | 11-Special Issue

Pages: 979-985

DOI: 10.5373/JARDCS/V11SP11/20193124