Texture-Based Image Analysis For the Assessment of Soybeans

Abstract

At 4.44 billion bushels worth an estimated $57.7 billion dollars, soybeans are one of the most produced crops in the United States and are appraised using a standard grading system set by the USDA(NASS, 2022). The grading and inspection process is based on twenty factors, including six that are visual based (USDA, 2020). The aim of this research is to apply texture-based image analysis to assess visual indicators of damage in soybeans and demonstrate potential use in increasing efficiency and consistency in soybean grading. Surface texture is one of the characteristics that is examined by inspectors as part of assessing soybean damage, while “image texture” is a calculated set of parameters that are used in image processing and analysis to quantify the apparent actual texture captured in an image. In this study, texture analysis using Haralick textural features is performed on sets of soybean images to assess damage types (as defined by the USDA inspection handbook and visual reference images) and improve soybean classification.