Artificial Neural Based Quality Assessment of Guava Fruit

Abstract

Merchants and food services are very competitive, and food manufacturers are under considerable pressure not to increase end-product costs when production costs go up. External pressures causing physical texture changes or chemical color, smell, and taste alterations induce fruit tissue injury. To examine this, a novel technique is proposed to identify surface and subsurface flaws utilizing a thermal picture and ordinary digital image concerning the original guava. This study discovered thermal data for thermal image capturing on the guava samples stored in the atmosphere with a FLIR-C2 thermal camera. A systematic Image processing approach was employed to distinguish the injured tissues of the fruits that were unaffected. The total result of the identification of thermal blushes is 95 %, whereas the result of standard image processing is 62.5 %. Theory permitted to differentiate the sound from the defected fruit scorched by birds or insects in creating food processing safety policies, developing the passive thermographic method.