Resumen
- Due to the computational power and memory of modern computers, computer vision techniques and neural networks can be used to develop a visual inspection system of agricultural products to satisfy product quality requirements. This chapter employs artificial vision techniques to classify seeds in RGB images. As a first step, an algorithm based on pixel intensity threshold is developed to detect and classify a set of different seed types, such as rice, beans, and lentils. Then, the information inferred by this algorithm is exploited to develop a neural network model, which successfully achieves learning classification and detection tasks through a semantic-segmentation scheme. The applicability and satisfactory performance of the proposed algorithms are illustrated by testing with real images, achieving an average accuracy of 92% in the selected set of classes. The experimental results verify that both algorithms can directly detect and classify the proposed set of seeds in input RGB images.