Deep Learning Techniques for Rice Kernel Defect Detection and Multi Classification using Yolo-V8 Model

In many parts of the world, rice is the main source of food, and the quality of the grain affects both consumer health and the financial interests of agricultural traders who make offers to farmers based on the quality of the rice kernels they have harvested.
When trading rice, the buyer must first assess the rice's quality before deciding on the purchase price. In the past, this employment relied
heavily on manual labour. Professionals are needed to hand choose and weigh various varieties of rice from samples that were chosen at
random. Calculating the weight ratio of faulty rice in the total sample size is how rice grades are determined. Rice quality estimate by
hand is time-consuming since skilled inspectors must recognise, pick up, and carefully weigh each defective kernel individually. This
study suggests a novel approach for automatic quality estimation of rice kernels in an effort to address these issues that the existing
literature is unable to address and to replace human labour-intensive operations with automated procedures. The main categories of
novel rice kernel faults being detected in this initiative include broken kernels (BR), spotted kernels (SP), yellow-colored kernels (YC),
mass-chalky kernels (MC), and partial-chalky kernels (PC). This experiment used a multi-stage classification strategy to do multi-classification of rice flaws, allowing a single kernel with dual defects to be discovered and then classified using Yolo v8. This
approach allowed for the detection and classification of rice kernels with diverse sorts of defects