Image-based weight estimation and grading of post-harvested Indian potato (Solanum tuberosum) using dimensional analysis integrated with adaptive neuro fuzzy inference system approach

Abstract

ABSTRACT The current study builds a model of the link between the image-based weights of Indian Potatoes extracted using the built computer vision system using a dimensional analysis integrated with the adaptive neuro-fuzzy inference system. An atomized sorting system must be developed to enhance the exportation quality. A soft computing method is applied to nonlinear data training. The features include size (dimensions, area, and volumes), shapes, and gravimetric properties were chosen. As per standards and export criteria, potatoes should be divided into three groups (small, medium, and big) based on their estimated weight. A self-build image-based system was designed for capturing an image. The experimental outcomes were contrasted with the presented integrated predicted outcomes. A respectable level of agreement is noted with good competency and dependability by the strong correlation coefficient (0.9975). Hence, the presented approach is strongly recommended for image-based weight estimation and classification. Code: https://colab.research.google.com/drive/1g-B81q8284Hw1QFwAJjqpmDEV35KsNzV