Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

Establishing a system for measuring plant health and bacterial infection is critical in agriculture. Previously, the farmers themselves, who observed them with their eyes relied on experience analysis, which could have been incorrect. Plant inspection can determine plants reflect quantity of green light near-infrared using infrared light, both visible eye drone. The goal this study was to create algorithms assessing infections rice images from unmanned aerial vehicles (UAVs) an ensemble classification technique. Convolution neural networks image were used. To convey interest, rice’s inside photo detected. project entailed pictures identify illnesses rice. shape distinct characteristics each observed. Rice symptoms defined machine learning processing techniques. Two steps convolution network based UAV used whether area will be affected by bacteria. proposed utilized classify types deceases accuracy rate 89.84 percent.